Past Workshops
This page provides information for our past workshops.
You can jump to a specific workshop (within this overall page) using the links below:
- 2023 Advanced Workshop
- 2023 Main Workshop
- 2022 Advanced Workshop
- 2022 Main Workshop
- 2018 Advanced Workshop
- 2018 Main Workshop
- 2017 Main Workshop
- 2015 Advanced Workshop
- 2015 Main Workshop
- 2014 Advanced Workshop
- 2014 Main Workshop
- 2013 Main Workshop
- 2013 Advanced Workshop
- 2012 Main Workshop
- 2011 Frequentist Workshop
- 2010 Frequentist Workshop
- 2011 Bayesian Workshop
2023 Main Causal Inference Workshop
Sunday (afternoon) – Thursday (morning), August 13-17, 2023
We would like to invite you to attend the Advanced Workshop on Research Design for Causal Inference. This workshop assumes knowledge at the level of the main workshop, and covers selected advanced topics in causal inference.
What’s special about these workshops are the speakers. The session will be taught by world-class causal inference researchers, who are experts in the topics they will discuss.
In person registration is limited to 125 participants for each workshop.
There will also be a Zoom option, but please come in-person if you can. The online experience is not the same.
Location
Northwestern Pritzker School of Law
375 East Chicago Avenue, Chicago, IL
Overview | Teaching Faculty and Organizers | Schedule | Registration | Hotels
Advanced Workshop Overview
The advanced workshop provides in-depth discussion of selected topics that are beyond what we can cover in the main workshop. The principal topics for 2023 are application of machine learning methods to causal inference; when and how to cluster standard errors, quantile and nonlinear difference-in-differences, doubly robust estimation of causal effects; difference-in-differences methods for staggered treatments (applied to different units at different times); and empirical Bayes approaches to estimating individual effects.
Target Audience
Empirical researchers who are familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge. We will assume familiarity, but not expertise, with potential outcomes, difference-in-differences, and panel data methods.
Teaching Faculty
- Christian Hansen (University of Chicago)
Christian Hansen is Wallace W. Booth Professor of Econometrics and Statistics at the University of Chicago, Booth School of Business. His research includes use of machine learning methods in estimation of causal and policy effects, estimation of panel data models, inference using clustered standard errors, quantile regression, and weak instruments. - Jeffrey Wooldridge (Michigan State University)
Jeffrey Wooldridge is University Distinguished Professor at Michigan State University and the author of leading undergraduate and graduate textbooks on econometrics. His research interests include causal inference and the econometrics of panel data, including nonlinear models in difference-in-differences and general policy analysis settings. - Brantly Callaway (University of Georgia)
Brantly Callaway is Associate Professor of Economics at the University of Georgia. His primary research interests are in microeconometrics, policy evaluation, and panel data, with a particular interest in developing methods that are robust to (and can be useful for learning about) treatment effect heterogeneity. Website - Christopher Walters (UC Berkeley)
Christopher Walters is Associate Professor of Economics at the University of California, Berkeley. His research focuses on the topics in labor economics and the economics of education, including early childhood programs, school effectiveness, and labor market discrimination.
Conference Organizers
- Bernard Black (Northwestern University)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Pritzker School of Law, the Institute for Policy Research, and the Kellogg School of Management, Finance Department. Principal research interests: health law and policy; empirical legal studies, law and finance. Papers on SSRN - Scott Cunningham (Baylor University)
Scott Cunningham is Professor of Economics at Baylor University. Principal research interests: mental healthcare; suicide; corrections; sex work; abortion policy; drug policy.
Advanced Workshop Outline
You should plan on full days, roughly 9:00-4:30 on Monday-Wednesday. Breakfast will be available at 8:30.
An informal wine-and cheese reception for all attendees will follow on Monday, August 14.
Sunday afternoon, August 13 (optional)
Primer on machine learning approaches to prediction
Christian Hansen
Introduction to "machine-learning" approaches to prediction algorithms, aimed at attendees with limited knowledge of machine learning methods. Shrinking a large set of potential predictors. Predicting without overpredicting: training and test sets; cross-validation. Lasso, regression trees, random forests, and deep nets. High-dimensional model selection (function classes, regularization, tuning). Combining models (ensemble models, bagging, boosting), model evaluation, and implementation.
Monday, August 14
Applications of machine learning to causal inference
Christian Hansen
When and how can machine learning methods be applied to causal inference questions. Limitations (prediction vs estimation) and opportunities (data pre-processing, prediction as quantity of interest, high-dimensional nuisance parameters), with examples from an emerging empirical literature.
Tuesday, August 15
Advanced matching and balancing methods
Jeffrey Wooldridge
Choosing among the many available matching and balancing methods. Estimators that aim directly at covariate balance. Combining balancing with regression and doubly robust estimators in cross-sectional and panel data settings. Synthetic controls.
Wednesday, August 16
Advanced panel data methods
Brantly Callaway
New developments in causal inference with panel data with an emphasis on methods that can be implemented with “short” panels (in general) and difference-in-differences (in particular). Limitations of two-way fixed effects regressions in this context. Comparison of alternative estimation strategies that have been proposed to address these weaknesses. Ways to weaken the parallel trends assumption and to diagnose and/or deal with violations of parallel trends. Introduction to recent work on dealing with more complicated treatment regimes.
Thursday morning, August 17
Empirical Bayes methods
Christopher Walters
Empirical Bayes methods for studying heterogeneity and estimating individual effects in settings with many unit-specific parameters (e.g., school, teacher, or physician quality; neighborhood effects on economic mobility; firm effects on wages; employer-specific labor market discrimination). Topics will include methods for quantifying variation in effects, empirical Bayes shrinkage for estimating individual effects, and connections to multiple testing and decision theory.
Stata and R coding
On selected days, we will run parallel Stata and R sessions, following the main lectures, to illustrate code for the research designs discussed in the lectures. Some speakers will also build Stata or R code into their lecture slides. Presenters: Scott Cunningham (Stata) and Joshua Lerner (R).
We will also provide a repository (likely on GitHub) of datasets and code to illustrate the methods presented in the workshop.
Registration and Workshop Cost
The workshop fee includes all materials, breakfast, lunch, snacks, and an evening reception on the first workshop day.
Main Workshop: Tuition is $900 ($600 for post-docs and graduate students; $500 if you are Northwestern-affiliated). The workshop fee includes all materials, breakfast, lunch, snacks, and an evening reception on the first workshop day.
Advanced Workshop: Tuition is $650 ($450 for post-docs and graduate students; $300 if you are Northwestern affiliated).
Discount for attending both workshops: There is a $250 discount for non-Northwestern persons attending both workshops, for combined cost of $1,300 ($800 for post-docs and graduate students ($150 additional discount for Northwestern affiliates).
Zoom option: We are charging the same amount for in-person and virtual attendance. Partly, we want to encourage in-person attendance. We also want to allow attendees to switch from one format to the other.
We know the workshop is not cheap. We use the funds to pay our speakers and expenses. Professor Black does not pay himself.
You can cancel either workshop five weeks in advance, for a 75% refund – July 3, 2023, for the Main Workshop and July 10, 2023, for the Advanced Workshop – or carry over your registration to next year for full credit. There is a 50% refund after these dates but before three weeks in advance, July 17, 2023, for the Main Workshop and July 24, 2023, for the Advanced Workshop. After these dates no refund will be given, because we can’t realistically replace you. But you can carry over the registration fee to a future workshop.
Hotels
To be announced.
Many attendees find an Airbnb or equivalent to be a good option.
Questions about the Workshop
Please email Bernie Black or Scott Cunningham for substantive questions or fee waiver requests, and Sebastian Bujak for logistics and registration.
2023 Advanced Causal Inference Conference
Monday – Friday, August 7-11, 2023
We are excited to be holding our 12th annual workshop on Research Design for Causal Inference at Northwestern Pritzker School of Law in Chicago, IL. We invite you to attend.
Our Advanced Workshop on Research Design for Causal Inference will be held this year on Sunday afternoon, August 13 through Thursday morning, August 17.
What’s special about these workshops are the speakers. The session will be taught by world-class causal inference researchers, who are experts in the topics they will discuss. In person registration is limited to 125 participants for each workshop.
There will also be a Zoom option, but please come in-person if you can. The online experience is not the same.
Location
Northwestern Pritzker School of Law
375 East Chicago Avenue, Chicago, IL 60611
Overview | Teaching Faculty and Organizers | Schedule | Registration | Hotels
Main Workshop Overview
We will cover the design of true randomized experiments and contrast them to natural or quasi experiments and to pure observational studies, where part of the sample is treated, the remainder is a control group, but the researcher controls neither which units are treated vs. control, nor administration of the treatment. We will assess the causal inferences one can draw from specific "causal" research designs, threats to valid causal inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods. We will begin instead with the goal of causal inference, and how to design a research plan to come closer to that goal, using messy, real-world datasets with limited sample sizes. The methods are often adapted to a particular study.
Target Audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc.), medicine, sociology, education, psychology, etc. – anywhere that causal inference is important.
We will assume knowledge, at the level of an upper-level undergraduate econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables. This course should be suitable both for researchers with recent PhD-level training in econometrics and for empirical scholars with reasonable but more limited training.
Teaching Faculty
In order of appearance:
- Donald Rubin (Harvard University)
- Donald Rubin is John L. Loeb Professor of Statistics Emeritus, at Harvard. His work on the “Rubin Causal Model” is central to modern understanding of causal inference with observational data. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia page
- Yiqing Xu (Stanford University)
- Yiqing Xu is Assistant Professor of Political Science at Stanford University. His main methods research involves causal inference with panel data. Website
- Matias Cattaneo (Princeton University)
Matias Cattaneo is Professor in the Department of Operations Research and Financial Engineering at Princeton University, with positions in Princeton's Department of Economics, Center for Statistics and Machine Learning, and Program in Latin American Studies. His research focus is on econometrics, statistics, data science and decision science, with particular interests in program evaluation and causal inference.
Conference Organizers
- Bernard Black (Northwestern University)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Pritzker School of Law, the Institute for Policy Research, and the Kellogg School of Management, Finance Department. Principal research interests: health law and policy; empirical legal studies, law and finance. Papers on SSRN - Scott Cunningham (Baylor University)
Scott Cunningham is Professor of Economics at Baylor University. Principal research interests: mental healthcare; suicide; corrections; sex work; abortion policy; drug policy.
Workshop Schedule
Plan on full days, roughly 9:00-4:30. Breakfast will be available at 8:30.
There will be afternoon wine and cheese receptions for all attendees following the main sessions on Monday, August 7 and Thursday, August 10.
Monday, August 7
Introduction to Modern Methods for Causal Inference
Donald Rubin
Overview of causal inference and the Rubin "potential outcomes" causal model. The "gold standard" of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Causal inference as a missing data problem, and imputation of missing potential outcomes. Experimental design and applications to observational studies. One-sided and two-sided noncompliance.
Readings
- Imbens, Guido, and Donald Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences (2015), chapters 1-8.
Tuesday, August 8
Matching and Reweighting Designs for "Pure" Observational Studies
Yiqing Xu,
The core, untestable requirement of selection [only] on observables. Ensuring covariate balance and common support. Matching, reweighting, and regression estimators of average treatment effects. Propensity score methods.
Readings
- Crump, Richard K, V. Joseph Hotz, Guido W. Imbens, and Oscar A. Mitnik (2009), Dealing with limited overlap in estimation of average treatment effects, 96 Biometrika 187-199
- Imbens, Guido W., and Jeffrey M. Wooldridge (2009), Recent developments in the econometrics of program evaluation, 47 Journal of Economic Literature 5-86.
- Imbens, Guido W. Matching Methods in Practice: Three examples. Journal of Human Resources 50.2 (2015): 373-419.
- Khan, Shakeeb, and Elie Tamer (2010), Irregular Identification, Support Conditions, and Inverse Weight Estimation, 78 Econometrica 2021-2042.
- Rosenbaum, Paul R., and Donald B. Rubin (1983), The Central Role of the Propensity Score in Observational Studies for Causal Effects, 70 Biometrika 41-55.
- Sasaki, Yuya, and Takuya Ura (2022), Inference for moments of ratios with robustness against large trimming bias and unknown convergence rate, 38 Econometric Theory 66 – 112.
- Seaman, Shaun R., and Stijn Vansteelandt (2018), Introduction to Double Robust Methods for Incomplete Data, 33 Statistical Science 184-197.
- Stuart, Elizabeth A. (2010), Matching methods for causal inference: A review and a look forward, 35 Statistical Science 1-21.
Wednesday, August 9
Panel Data and Difference-in-Differences
Yiqing Xu,
Panel data methods: pooled OLS, random effects, and fixed effects. Simple two-period DiD and panel data extensions. The core "parallel trends" assumption. Testing this assumption. Event study (leads and lags) and distributed lag models. Accommodating covariates. Triple differences. Robust and clustered standard errors.
Readings
- Callaway, Brantly, and Pedro H. C. Sant’Anna (2021), Difference-in-Differences with Multiple Time Periods, 225 Journal of Econometrics 200-230.
- Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe (2023), What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature,235 Journal of Econometrics 2218-2244
- Roth, Jonathan, and Pedro H. C. Sant’Anna (2023), When Is Parallel Trends Sensitive to Functional Form?, 91 Econometrica 737-747.
Thursday, August 10
Regression Discontinuity
Matias Cattaneo
Regression discontinuity (RD) designs: sharp and fuzzy designs; continuity-based methods and bandwidth selection; local randomization methods and window selection; empirical falsification of RD assumptions; extensions and generalizations of canonical RD setup: discrete running variable, multi-cutoff, multi-score, and geographic designs. RD software website
Readings
See https://rdpackages.github.io/, but especially:
- Cattaneo, Matias, Nicolas Idrobo, and Rocio Titiunik, A Practical Introduction to Regression Discontinuity Designs: Foundations (2019)
- Cattaneo, Matias, Nicolas Idrobo, and Rocio Titiunik, A Practical Introduction to Regression Discontinuity Designs: Extensions (2022)
Friday, August 11: Morning
Instrumental Variable Methods
Matias Cattaneo
Causal inference with instrumental variables (IV): the role of the exclusion restriction and first stage assumption; the monotonicity assumption and local average treatment effect (LATE) interpretation; applications to randomized experiments with imperfect compliance, including intent-to-treat designs and two-stage estimation. Connections between IV and fuzzy RD designs.
Readings
- Imbens and Rubin, chapters 23-24.
- Angrist, Joshua D., and Jorn-Steffen Pischke, Mostly Harmless Econometrics (2009), chap. 4.
- Angrist, Joshua, Guido Imbens, and Donald Rubin (1996), Identification of Causal Effects Using Instrumental Variables, 91 Journal of the American Statistical Association 444-455.
- Imbens and Rubin, chapter 25 (Bayesian approach to IV)
Friday, August 11: Afternoon
Feedback on your own research
Attendees will present their own research design questions from current work in breakout sessions and receive feedback on research design. Session leaders: Bernie Black, Scott Cunningham, Matias Cattaneo, Rocio Titiunik, or Joshua Lerner. Additional parallel sessions if needed to meet demand.
Stata and R coding
On selected days, we will run parallel Stata and R sessions, following the main lectures, to illustrate code for the research designs discussed in the lectures. Some speakers will also build Stata or R code into their lecture slides. Presenters: Scott Cunningham (Stata) and Joshua Lerner (R).
We will also provide a repository (likely on GitHub) of datasets and code to illustrate the methods presented in the workshop.
Registration and Workshop Cost
The workshop fee includes all materials, breakfast, lunch, snacks, and the receptions.
Main Workshop: Tuition is $900 ($600 for post-docs and graduate students; $500 if you are Northwestern-affiliated).
Advanced Workshop: Tuition is $650 ($450 for post-docs and graduate students; $300 if you are Northwestern affiliated).
Discount for attending both workshops: There is a $250 discount for non-Northwestern persons attending both workshops, for combined cost of $1,300 ($800 for post-docs and graduate students ($150 additional discount for Northwestern affiliates).
Zoom option: We are charging the same amount for in-person and virtual attendance. Partly, we want to encourage in-person attendance. We also want to allow attendees to switch from one format to the other.
We know the workshop is not cheap. We use the funds to pay our speakers and expenses. Professor Black does not pay himself.
You can cancel either workshop five weeks in advance, for a 75% refund – July 3, 2023, for the Main Workshop and July 10, 2023, for the Advanced Workshop – or carry over your registration to next year for full credit. There is a 50% refund after these dates but before three weeks in advance, July 17, 2023, for the Main Workshop and July 24, 2023, for the Advanced Workshop. After these dates no refund will be given, because we can’t realistically replace you. But you can carry over the registration fee to a future workshop.
Hotels
Many attendees find an Airbnb or equivalent to be a good option. Please ask for Northwestern rates when you book your hotel.
Questions about the Workshop
Please email Bernie Black or Scott Cunningham for substantive questions or fee waiver requests, and Sebastian Bujak for logistics and registration.
2022 Advanced Causal Inference Conference
Monday – Wednesday, August 15-17, 2022
We would like to invite you to attend the Advanced Workshop on Research Design for Causal Inference. This workshop assumes knowledge at the level of the main workshop, and covers selected advanced topics in causal inference.
Location
Northwestern Pritzker School of Law
375 East Chicago Avenue, Chicago, IL
Overview | Teaching Faculty and Organizers | Registration | Schedule | Hotels
Advanced Workshop Overview
The advanced workshop provides in-depth discussion of selected topics that are beyond what we can cover in the main workshop. The principal topics for 2022 quantile and nonlinear difference-in-differences, doubly robust estimation of causal effects; DiD methods that address staggered treatments (applied to different units at different times); and the application of machine learning methods to causal inference.
Target Audience for Advanced Workshop
Empirical researchers who are familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge. We will assume familiarity, but not expertise, with potential outcomes, difference-in-differences, and panel data methods.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
-
Jeffrey Wooldridge (Michigan State University)
Jeffrey Wooldridge is University Distinguished Professor at Michigan State University and the author of leading undergraduate and graduate textbooks on econometrics. His research interests include causal inference and the econometrics of panel data, including nonlinear models in difference-in-differences and general policy analysis settings. -
Yiqing Xu (Stanford University)
Yiqing Xu is Assistant Professor of Political Science at University of California, San Diego. His main methods research involves causal inference with panel data. -
Christian Hansen (University of Chicago)
Christian Hansen is Wallace W. Booth Professor of Econometrics and Statistics at the University of Chicago, Booth School of Business. His research has chiefly been in the areas of the use of machine learning methods in estimation of causal and policy effects, estimation of panel data models, inference using clustered standard errors, quantile regression, and weak instruments.
Conference Organizers
- Bernard Black (Northwestern University)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Pritzker School of Law, the Institute for Policy Research, and the Kellogg School of Management, Finance Department. Principal research interests: health law and policy; empirical legal studies, law and finance, international corporate governance. Papers on SSRN - Scott Cunningham (Baylor University)
Scott Cunningham is Professor of Economics at Baylor University. Principal research interests: mental healthcare; suicide; corrections; sex work; abortion policy; drug policy.
Registration and Workshop Cost
Tuition for the Advanced Workshop is $600 ($400 for post-docs and graduate students; $300 if you are Northwestern affiliated).
Tuition for the Main Workshop is $900 ($600 for post-docs and graduate students PhD, SJD, or law; $500 if you are Northwestern affiliated).
There is a $200 discount for attending both workshops ($100 if Northwestern-affiliated).
Zoom option: We’ve decided to charge the same amount for in-person and virtual attendance. Partly, we want to encourage in-person attendance. We also want to allow attendees to switch from one format to the other, depending on how travel and COVID-19 risk looks by mid-summer.
Vaccination Recommended: Northwestern no longer requires proof of vaccination for on-campus events. However, we still strongly recommend that you be vaccinated against COVID-19 (2-doses) and have received a booster shot if the second dose was more than 6 months before the start of the workshop. If you have been infected with COVID – especially if recently infected with the Omicron variant – this can be considered as the rough equivalent to one vaccine dose.
For special circumstances, please contact Professor Black at bblack@northwestern.edu.
The workshop fees include all materials, breakfast, lunch, snacks, and an evening reception on the first workshop day.
We know the workshop is not cheap. We use the funds to pay our speakers and expenses; we don’t pay ourselves.
Registration is limited to 100.
You can cancel by July 8, 2022 for a 75% refund (or carry over your registration to next year for full credit) and by July 22, 2022 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that, because we can't realistically replace you. If the workshop is canceled, we will offer a full refund.
Workshop Schedule
Plan on full days, roughly 9:00-5:00. Breakfast will be available at 8:30.
Monday, August 15
Advanced matching and balancing methods
Jeffrey Wooldridge
Choosing among the many available matching and balancing methods. Estimators that aim directly at covariate balance. Combining balancing with regression and doubly robust estimators in cross-sectional and panel data settings. Synthetic controls.
Tuesday, August 16
Advanced panel data methods
Yiqing Xu
Causal inference with panel data using parametric, semi-parametric, non-parametric methods for addressing imbalance between treated and control units. Bias in classic DiD models using two-way fixed effects. Topics include interactive fixed effects and matrix completion methods, as well as reweighting approaches such as panel matching, trajectory balancing and augmented synthetic control. Relative strengths and weaknesses of different methods will be discussed.
Wednesday, August 17
Introduction to machine learning (predictive inference)
Christian Hansen
Introduction to “machine-learning” approaches to prediction algorithms. High-dimensional model selection (function classes, regularization, tuning), model combination (ensemble models, bagging, boosting), model evaluation, and implementation.
Applications of machine learning to causal inference
Christian Hansen
When and how can machine learning methods be applied to causal inference questions. Limitations (prediction vs estimation) and opportunities (data pre-processing, prediction as quantity of interest, high-dimensional nuisance parameters), with examples from an emerging empirical literature.
Hotels
There are many hotels within walking distance to Northwestern Law School, in all price ranges. There are also many air B&B options. If you want to share with another attendee, please send an email to Sebastian Bujak at sebastian.bujak@law.northwestern.edu and we will circulate a list of interested attendees as we get closer to the workshop data. Please be aware that hotel rates will likely increase as we get closer to the conference.
Millennium Knickerbocker Hotel
- 163 E Walton Place, 0.5 miles from Northwestern
- Current Rate as of 4/13/22: $105
- 100 E Chestnut Ave, 0.5 miles from Northwestern
- Current Rate as of 4/13/22: $108
- Please note, they have shared room options.
- 701 N Michigan Ave, 0.4 miles from Northwestern
- Current Rate as of 4/13/22: $166
- 160 E Huron St , 0.4 miles from Northwestern
- Current Rate as of 4/13/22: $175
Questions about the Workshop
Please email Bernie Black or Scott Cunningham for substantive questions or fee waiver requests, and Sebastian Bujak for logistics and registration.
2022 Main Causal Inference Workshop
Monday – Friday, August 8-12, 2022
After a COVID break during 2020 and 2021, we are excited to be holding our 11th annual workshop on Research Design for Causal Inference at Northwestern Law School in Chicago, IL. We invite you to attend.
What’s special about this workshop are the speakers. The workhop will be taught by world-class causal inference researchers. In the past we have filled the main workshop quickly, and we expect there may be pent up demand after the 2-year COVID break, so please register soon.
Our Advanced Workshop on Research Design for Causal Inference will be held this year on August 15-17, 2022.
Location
Northwestern Pritzker School of Law
375 East Chicago Avenue, Chicago, IL 60611
Overview | Teaching Faculty and Organizers | Registration | Schedule | Hotels
Main Workshop Overview
We will cover the design of true randomized experiments and contrast them to natural or quasi experiments and to pure observational studies, where part of the sample is treated in some way, the remainder is a control group, but the researcher controls neither which units are treated vs. control, nor administration of the treatment. We will assess the causal inferences one can draw from specific “causal” research designs, threats to valid causal inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods. We will begin instead with the goal of causal inference, and how to design a research plan to come closer to that goal, using messy, real-world datasets with limited sample sizes. The methods are often adapted to a particular study.
Target Audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc.), medicine, sociology, education, psychology, etc. – anywhere that causal inference is important.
We will assume knowledge, at the level of an upper-level college econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables. This course should be suitable both for researchers with recent PhD-level training in econometrics and for empirical scholars with reasonable but more limited training.
Teaching Faculty
- Donald Rubin (Harvard University)
Donald Rubin is John L. Loeb Professor of Statistics Emeritus, at Harvard. His work on the “Rubin Causal Model” is central to modern understanding of causal inference with observational data. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia page
- Pedro Sant’Anna (Vanderbilt University and Microsoft)
Pedro SantAnna is Assistant Professor of Economics at Vanderbilt University. His research focus is on microeconometrics, including causal inference methods and program evaluation. - Rocio Titiunik (Princeton University)
Rocío Titiunik is Professor of Politics at Princeton University. She specializes in quantitative methodology for the social sciences, with emphasis on quasi-experimental methods for causal inference.
- Matias Cattaneo (Princeton University)
Matias Cattaneo is Professor in the Department of Operations Research and Financial Engineering at Princeton University, with positions in Princeton’s Department of Economics, Center for Statistics and Machine Learning, and Program in Latin American Studies. His research focus is econometrics, statistics, data science and decision science, with particular interests in program evaluation and causal inference.
Conference Organizers
- Bernard Black (Northwestern University)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Pritzker School of Law, the Institute for Policy Research, and the Kellogg School of Management, Finance Department. Principal research interests: health law and policy; empirical legal studies, law and finance, international corporate governance. Papers on SSRN
- Scott Cunningham (Baylor University)
Scott Cunningham is Professor of Economics at Baylor University. Principal research interests: mental healthcare; suicide; corrections; sex work; abortion policy; drug policy.
Registration and Workshop Cost
Tuition for the Main Workshop is $900 ($600 for post-docs and graduate students PhD, SJD, or law; $500 if you are Northwestern affiliated).
Tuition for the Advanced Workshop is $600 ($400 for post-docs and graduate students; $300 if you are Northwestern affiliated).
There is a $200 discount for attending both workshops ($100 if Northwestern-affiliated).
Zoom option: We’ve decided to charge the same amount for in-person and virtual attendance. Partly, we want to encourage in-person attendance. We also want to allow attendees to switch from one format to the other, depending on how travel and COVID-19 risk looks by mid-summer.
Vaccination Recommended: Northwestern no longer requires proof of vaccination for on-campus events. However, we still strongly recommend that you be vaccinated against COVID-19 (2-doses) and have received a booster shot if the second dose was more than 6 months before the start of the workshop. If you have been infected with COVID – especially if recently infected with the Omicron variant – this can be considered as the rough equivalent to one vaccine dose.
For special circumstances, please contact Professor Black at bblack@northwestern.edu.
The workshop fee includes all materials, breakfast, lunch, snacks, and an evening reception on the first workshop day.
We know the workshop is not cheap. We use the funds to pay our speakers and expenses; we don’t pay ourselves.
Registration is limited to 125 participants.
You can cancel by July 1, 2022 for a 75% refund (or carry over your registration to next year for full credit) and by July 15, 2022 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that, because we can't realistically replace you. If the workshop is canceled, we will offer a full refund.
Workshop Schedule
Plan on full days, roughly 9:00-5:00. Breakfast will be available at 8:30.
Monday, August 8
Introduction to Modern Methods for Causal Inference
Donald Rubin
Overview of causal inference and the Rubin “potential outcomes” causal model. The “gold standard” of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Causal inference as a missing data problem, and imputation of missing potential outcomes. Rerandomization. One-sided and two-sided noncompliance.
Tuesday, August 9
Matching and Reweighting Designs for “Pure” Observational Studies
Pedro Sant’Anna
The core, untestable requirement of selection [only] on observables. Ensuring covariate balance and common support. Matching, reweighting, and regression estimators of average treatment effects. Propensity score methods.
Wednesday, August 10
Panel Data and Difference-in-Differences
Pedro Sant’Anna
Panel data methods: pooled OLS, random effects, and fixed effects. Simple two-period DiD and panel data extensions. The core “parallel trends” assumption. Testing this assumption. Event study (leads and lags) and distributed lag models. Accommodating covariates. Triple differences. Robust and clustered standard errors.
Thursday, August 11
Regression Discontinuity
Rocio Titiunik or Matias Cattaneo
Regression discontinuity (RD) designs: sharp and fuzzy designs; continuity-based methods and bandwidth selection; local randomization methods and window selection; empirical falsification of RD assumptions; extensions and generalizations of canonical RD setup: discrete running variable, multi-cutoff, multi-score, and geographic designs. RD software website
Friday, August 12: Morning
Instrumental variable methods
Matias Cattaneo or Rocio Titiunik
Causal inference with instrumental variables (IV) : th e role of the exclusion restriction and first stage assumption; the monotonic ity assumption and local average treatment effect (LATE) interpretation; applications to randomized experiments with imperfect compliance , including intent-to-treat designs and two-stage estimation. Connections between IV and fuzzy RD designs.
Friday, August 12: Afternoon
Feedback on your own research
Attendees will present their own research design questions from current work in breakout sessions and receive feedback on research design. Session leaders: Bernie Black, Scott Cunningham, Rocio Titiunik or Matias Cattaneo). Additional parallel sessions if needed to meet demand.
Stata and R code
On selected days, we will run parallel Stata and R sessions to illustrate code for the research designs discussed in the lectures, or the speakers will build Stata code into their lecture slides.
Presenters: Bernard Black (Stata) and Joshua Lerner (R)
Hotels
There are many hotels within walking distance to Northwestern Law School, in all price ranges. There are also many air B&B options. If you want to share with another attendee, please send an email to Sebastian Bujak at sebastian.bujak@law.northwestern.edu and we will circulate a list of interested attendees as we get closer to the workshop data. Please be aware that hotel rates will likely increase as we get closer to the conference.
Millennium Knickerbocker Hotel
- 163 E Walton Place, 0.5 miles from Northwestern
- Current Rate as of 4/13/22: $105
- 100 E Chestnut Ave, 0.5 miles from Northwestern
- Current Rate as of 4/13/22: $108
- Please note, they have shared room options.
- 701 N Michigan Ave, 0.4 miles from Northwestern
- Current Rate as of 4/13/22: $166
- 160 E Huron St , 0.4 miles from Northwestern
- Current Rate as of 4/13/22: $175
Questions about the Workshop
Please email Bernie Black or Scott Cunningham for substantive questions or fee waiver requests, and Sebastian Bujak for logistics and registration.
2018 Advanced Causal Inference Workshop
We would like to invite you to attend the Fourth Annual Advanced Workshop on Research Design for Causal Inference, which builds on our "main" workshop.
Monday-Wednesday, June 25-27, 2018, at Northwestern Pritzker School of Law, 375 East Chicago Avenue, Chicago, IL
Our regular "Main" Workshop on Research Design for Causal Inference will be held this year on June 18-22, 2018, at Northwestern Pritzker School of Law.
Workshop Overview
The advanced workshop provides in-depth discussion of selected topics that are beyond what we can cover in the main workshop. Principal topics for 2018 include: Day 1 (Mon.): Principal stratification (generalization of causal-IV concepts and applications, including sample censoring through death or attrition. Day 2 (Tues.): Direct and indirect causal effects. Synthetic controls and other advanced “matching” approaches with emphasis on panel data sets. Day 3 (Wed.): Application of machine learning methods to causal inference.
Target Audience for Advanced Workshop
Empirical researchers who are reasonably familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge. We will assume familiarity with potential outcomes notation, difference-in-differences, regression discontinuity, panel data, and instrumental variable designs, but will not assume expertise in any of these areas.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. The faculty are listed in order of appearance.
- Donald Rubin (Harvard University, Department of Statistics)
Rubin is John L. Loeb Professor of Statistics, Harvard University. His work on the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia page - Fabrizia Mealli (University of Florence, Department of Statistics and Computer Science)
Fabrizia Mealli is Professor of Statistics at the University of Florence and external research associate at the Institute for Social and Economic Research (ISER) at the University of Essex. Her research focuses on causal inference and simulation methods, program evaluation, missing data, and Bayesian inference. She is a fellow of the American Statistical Association, and associate editor of Journal of the American Statistical Association (JASA), Biometrics, and Annals of Applied Statistics. - Yiqing Xu (University of California, San Diego, Department of Political Science)
Yiqing Xu is Assistant Professor of Political Science at University of California, San Diego. His main methods research involves causal inference with panel data. - Justin Grimmer (University of Chicago, Department of Political Science)
Justin Grimmer is Associate Professor of Political Science at the University of Chicago. His primary research interests include political representation, Congressional institutions, and text as data methods.
Conference Organizers
- Bernard Black (Northwestern University)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Pritzker School of Law, the Institute for Policy Research, and the Kellogg School of Management, Finance Department. Principal research interests: health law and policy; empirical legal studies, law and finance, international corporate governance. Papers on SSRN - Mathew McCubbins (Duke University)
Professor of Political Science and Law at Duke University, with positions in the Political Science Department and the Law School, and director of the Center for Law and Democracy. Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decision-making; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN
Workshop Schedule
Monday, June 25 (Donald Rubin and Fabrizia Mealli):
Principal Stratification and Censoring (9:00am-12:00pm, 1:00-4:00pm)
Generalizing the causal-IV strata of compliers-always takers-never takers-defiers. Which treatment effects can be estimated for which strata? Handling missing data and censoring through “death” or attrition.
Monday Reception (4:00pm-5:30pm)
Tuesday, June 26 (Donald Rubin, Fabrizia Mealli, and Yiqing Xu):
Direct and Indirect Causal Effects (Donald Rubin and Fabrizia Mealli, 9:00am-12:00pm)
“Mediation” analysis: Direct and indirect causal effects versus principal associative and dissociative effects.
Tuesday Lunch Talk: Bloopers II: How Other Smart People Get Causal Inference Wrong (Bernie Black, 12:30-1:30pm)
Examples, drawn from different areas, of how to get causal inference wrong.
Advanced Matching (Yiqing Xu, 1:45pm-4:45pm)
Advanced matching and reweighting methods, with an emphasis on panel data applications. Generalized synthetic controls. Relative strengths and weaknesses of different matching and reweighting approaches.
Wednesday, June 27 (Justin Grimmer):
Machine learning (predictive inference) meets causal inference (9:00am-12:00pm, 1:10-4:00pm)
Introduction to machine learning approaches. When and how can machine learning methods be applied to causal inference questions.
2018 Ninth Annual Main Causal Inference Workshop
We would like to invite you to attend the Ninth Annual Workshop on Research Design for Causal Inference, sponsored by Northwestern University and Duke University.
Monday-Friday, June 18-22, 2018, at Northwestern Pritzker School of Law, 375 East Chicago Avenue, Chicago, IL
Our "Advanced" Workshop on Research Design for Causal Inference will be held this year on June 25-27, 2018, at Northwestern Pritzker School of Law.
Workshop Overview
Research design for causal inference is at the heart of a “credibility revolution” in empirical research. We will cover the design of true randomized experiments and contrast them to natural or quasi experiments and to pure observational studies, where part of the sample is treated in some way, the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the causal inferences one can draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods. We will begin instead with the goal of causal inference, and emphasize how to design research to come closer to that goal. The methods are often adapted to a particular study. Some of the methods are covered in PhD programs, but rarely in depth, and rarely with a focus on credible causal inference and which methods to use with messy, real-world datasets and limited sample sizes. Several workshop days will include a Stata “workshop” to illustrate selected methods with real data and Stata code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), medicine, sociology, education, psychology, etc. –anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables. Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training. Even for recent PhD’s, there will be much that you don’t know, or don’t know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. The faculty are listed in order of appearance.
- Donald Rubin (Harvard University, Department of Statistics)
Rubin is John L. Loeb Professor of Statistics, Harvard University. His work on the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia page - Justin McCrary (University of California, Berkeley, Law School)
Justin McCrary is Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies. - Jens Hainmueller (Stanford University, Department of Political Science)
Jens Hainmueller is Professor in the Stanford Political Science Department, and co-Director of the Stanford Immigration Policy Lab. He also holds a courtesy appointment in the Stanford Graduate School of Business. His research interests include statistical methods, political economy, and political behavior. Papers on SSRN
Conference Organizers
- Bernard Black (Northwestern University)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Pritzker School of Law, the Institute for Policy Research, and the Kellogg School of Management, Finance Department. Principal research interests: health law and policy; empirical legal studies, law and finance, international corporate governance. Papers on SSRN - Mathew McCubbins (Duke University)
Professor of Political Science and Law at Duke University, with positions in the Political Science Department and the Law School, and director of the Center for Law and Democracy. Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decision-making; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN
Workshop Schedule
Monday, June 18 (Donald Rubin):
Introduction to Modern Methods for Causal Inference (9:30am-12:30pm, 1:30pm-3:30pm)
Overview of causal inference and the Rubin “potential outcomes” causal model. The “gold standard” of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Causal inference as a missing data problem, and imputation of missing potential outcomes. Choosing estimands (the science), and how the estimand affects research design. One-sided and two-sided noncompliance.
Monday Reception (4:00-5:30pm)
Tuesday, June 19 (Justin McCrary):
Designs for “Pure” Observational Studies (9:00am-12:00pm, 1:45-3:45pm)
The core, untestable requirement of selection [only] on observables. Common support assumptions. Subclassification, matching, reweighting, and regression estimators of average treatment effects. Propensity score methods.
Tuesday Lunch Talk (Don Rubin, 12:30-1:30pm)
Some statistical bloopers
Stata-based examples (Bernie Black, 3:45-4:45pm)
SIntended as a gentle introduction, for people who know a little bit about Stata, to how to use Stata to implement some of the methods we will discuss during the week. If you are a novice Stata user, there are introductory materials on the course folder at Northwestern Box\Causal Inference Workshops\Stata and R materials.
R-based examples (Josh Lerner, 3:45-4:45pm)
Similar, but using R rather than Stata.
Wednesday, June 20 (Justin McCrary):
Instrumental variable methods (9:00am-12:00pm, 1:15-3:45pm)
Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.
Stata- and R-based examples (Bernie Black and Josh Lerner, 3:45-4:45pm)
Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.
Thursday, June 21 (Jens Hainmueller):
Panel Data and Difference-in-Differences (9:00am-12:00pm; 1:45-4:45pm)
Panel data methods: pooled OLS, random effects, correlated random effects, and fixed effects. Simple two-period DiD. The core “parallel changes” assumption. Testing this assumption. Leads and lags and distributed lag models. When does a design with unit fixed effects become DiD? Accommodating covariates. Triple differences. Robust and clustered standard errors.
Thursday lunch talk: Bloopers in Research Design: How Smart People Get Causal Inference Wrong (Bernie Black, 12:30-1:30pm)
Examples, drawn from different areas, of how to get causal inference wrong.
Friday, June 22 (Jens Hainmueller):
Regression Discontinuity (9:00am-12:00pm)
(Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; testing for covariate balance and manipulation of the threshold; discontinuities as substitutes for true randomization and sources of convincing instruments.
Friday lunch talk: Bloopers with Data: How to Really Examine Your Data (with Examples) (Bernie Black, 12:30-1:30pm)
Friday afternoon: Feedback on your own research (1:45-5:15pm)
Attendees will have an opportunity to present their own research design questions from current work in breakout sessions. Goal: obtain feedback on research design; not present results from a complete paper. [We ask presenters to stay for the full session, and can’t promise an early slot for those who must leave early.] (15 min to present, 15 min discussion). Session leaders: Bernie Black, Jens Hainmueller, Mat McCubbins; we’ll add additional sections as needed.
2017 Eighth Annual Main Causal Inference Workshop
We would like to invite you to attend the Eighth Annual Workshop on Research Design for Causal Inference, sponsored by Northwestern University and Duke University.
Monday-Friday, June 19-23, 2017, at Northwestern Pritzker School of Law, 375 East Chicago Avenue, Chicago, IL
Workshop Overview
Research design for causal inference is at the heart of a “credibility revolution” in empirical research. We will cover the design of true randomized experiments and contrast them to natural or quasi experiments and to pure observational studies, where part of the sample is treated in some way, the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the causal inferences one can draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods. We will begin instead with the goal of causal inference, and emphasize how to design research to come closer to that goal. The methods are often adapted to a particular study. Some of the methods are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to use with messy, real-world datasets and limited sample sizes. Several workshop days will include a Stata “workshop” to illustrate selected methods with real data and Stata code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), medicine, sociology, education, psychology, etc. –anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training. For recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. The faculty are listed in order of appearance.
- Donald Rubin (Harvard University, Department of Statistics)
Rubin is John L. Loeb Professor of Statistics, Harvard University. His work on the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia page - Alberto Abadie (Massachusetts Institute of Technology, Department of Economics)
Alberto Abadie is Professor of Economics at the Massachusetts Institute of Technology. Principal research interests: econometrics; program evaluation. Principal research interests: econometrics; program evaluation. Papers on SSRN - Jens Hainmueller is Associate Professor in the Stanford Political Science Department. He also holds a courtesy appointment in the Stanford Graduate School of Business. His research interests include statistical methods, political economy, and political behavior.
Conference Organizers
- Bernard Black (Northwestern University)
Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School, Institute for Policy Research, and Kellogg School of Management. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN - Mathew McCubbins (Duke University)
Professor of Political Science and Law at Duke University, with positions in the Law School and the Political Science Department, and director of the Center for Law and Democracy. Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decision-making; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN
Workshop Schedule
Monday, June 19 (Don Rubin): Introduction to Modern Methods for Causal Inference
Overview of causal inference and the Rubin “potential outcomes” causal model. The “gold standard” of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Causal inference as a missing data problem, and imputation of missing potential outcomes. Rerandomization. One-sided and two-sided noncompliance.
Tuesday, June 20 (Alberto Abadie): Designs for “Pure” Observational Studies
The core, untestable requirement of selection [only] on observables. Ensuring covariate balance and common support. Subclassification, matching, reweighting, and regression estimators of average treatment effects. Propensity score methods.
Wednesday, June 21 (Alberto Abadie): Instrumental variable methods
Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.
Thursday, June 22 (Jens Hainmueller): Panel Data and Difference-in-Differences
Panel data methods: pooled OLS, random effects, correlated random effects, and fixed effects. Simple two-period DiD. The core “parallel changes” assumption. Testing this assumption. Leads and lags and distributed lag models. When does a design with unit fixed effects become DiD? Accommodating covariates. Triple differences. Robust and clustered standard errors.
Friday morning, June 23 (Jens Hainmueller): Regression Discontinuity
(Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; testing for covariate balance and manipulation of the threshold; discontinuities as substitutes for true randomization and sources of convincing instruments.
Friday afternoon: Feedback on your own research
Attendees will present their own research design questions from current work in breakout sessions and receive feedback on research design. Session leaders: Bernie Black, Mat McCubbins, Jens Hainmueller. Additional parallel sessions if needed to meet demand.
Stata and R sessions
On Tuesday and Wednesday we will either run parallel Stata and R sessions to illustrate actual code to implement the designs discussed in the lectures. For Thursday and Friday, Jens Hainmueller will build Stata code into his lecture slides.
2015 Sixth Annual Advanced Causal Inference Workshop
We would like to invite you to attend our third "advanced" workshop on Research Design for Causal Inference. The workshop builds on our "main" workshop. The workshop is sponsored by Northwestern Law School, the Center for the Study of Democracy and the Rule of Law at Duke Law School, and the Society for Empirical Legal Studies.
Sunday-Wednesday, July 19-22, 2015, at Northwestern Law School, 375 East Chicago Avenue, Chicago, IL
Our regular "Main" Workshop on Research Design for Causal Inference will be held this year on July 13-17, 2015, at Northwestern Law School.
Workshop Overview
The advanced causal inference workshop seeks to provide an in-depth discussion of selected topics that are beyond what we can cover in the main workshop. Principal topics for 2014 include: Day 1: Choosing estimands (the science), and how choice of estimand affects research design. Principal stratification methods (a little known, but very powerful extension of the always taker/never-taker/complier/defier categories developed in “causal IV”); advanced matching methods; multiple imputation of missing potential outcomes. Day 2: Simulation studies; bootstrap methods; advanced topics in regression discontinuity design. Day 3: Causal inference with panel data. Topics will include handling treatment heterogeneity, handling time dynamics, synthetic controls, marginal structural models, and standard errors.
Target Audience for Advanced Workshop
Our target audience is empirical researchers who are reasonably familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge. We will assume familiarity with the potential outcomes notation, randomization inference, difference-in-differences, regression discontinuity, panel data, and instrumental variable designs, but will not assume expertise in any of these areas.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions (in order of appearance)
- Justin McCrary (University of California, Berkeley, Law School) Justin McCrary is Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies. Papers on SSRN
- Alberto Abadie (Harvard University, Kennedy School of Government) Alberto Abadie is Professor of Public Policy at the Kennedy School of Government at Harvard University. Principal research interests: econometrics; program evaluation. Papers on SSRN
- Tyler VanderWeele (Harvard University, School of Public Health) Tyler VanderWeele is Professor of Epidemiology and Professor of Biostatistics at the Harvard School of Public Health, and the author of Explanation in Causal Inference: Methods for Mediation and Interaction (Oxford University Press 2015). Principal research interests: causal inference; mediation, interaction and spillover; epidemiology; religion and health.
Conference Organizers
- Bernard Black (Northwestern University) Nicholas J. Chabraja Professor at Northwestern University School of Law, with a secondary appointment at Kellogg School of Management, Department of Finance. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN
- Mathew McCubbins (Duke University) Professor of Political Science and Law at Duke University, with positions in the Law School and the Political Science Department, and director of the Center for Law and Democracy. Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decision-making; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN
Workshop Schedule
General schedule: Breakfast available at 8:30. “Lecture” sessions will run roughly 9:00-noon, lunch noon-1:00, sessions 1:00-3:00. “Stata sessions”: 3:15-4:15. All times except starting times are approximate. Please plan to arrive Saturday evening. Variations for particular days are indicated below. Return travel: The Wednesday afternoon sessions will end by 5:00, a return flight leaving 7:00 or later from O’Hare is safe; a bit earlier from Midway.
Registration and welcome breakfast: July 19, 8:00 a.m.
Sunday-Monday July 19-20 (Justin McCrary)
Sunday: Simulation and bootstrap Conducting simulation studies. Inference and testing using the bootstrap, including adapting bootstrap methods to your research design. Different bootstrap flavors and asymptotic refinement.
Monday: Non-linear methods
Selected issues for non-linear models, including logit, conditional logit, probit, and count models. Using non-linear models with panel data. Maximum likelihood and quasi maximum likelihood estimation. Inconsistency of non-linear models with fixed effects.
Tuesday July 21 (Alberto Abadie)
Advanced Matching and Causal IV
Selected topics in matching on covariates. “Causal IV” with covariates and Abadie’s “kappa.”
Wednesday July 22 (Tyler VanderWeele)
Causal mediation analysis -- the direct and indirect effects of causes. Comparison of traditional social science approaches to potential outcomes methods. Identification. Regression-based methods. Sensitivity analysis. Multiple mediators.
2015 Sixth Annual Main Causal Inference Workshop
We would like to invite you to attend the Sixth annual workshop on Research Design for Causal Inference, sponsored by Northwestern University, Duke University, and the Society for Empirical Legal Studies.
Monday-Friday, July 13-17, 2015, at Northwestern Law School, 375 East Chicago Avenue, Chicago, IL
Note: The main workshop is close to capacity for 2015. We have reserved a limited number of spaces for: faculty attendees. Graduate students and post-docs who would like to attend should email Bernie Black to be added to the waiting list. In offering any available spots to persons on the waiting list, we will give priority to attendees who will attend both workshops.
An Advanced Workshop on Research Design for Causal Inference will be held this year on July 19-22, 2015, at Northwestern Law School.
Workshop Overview
Research design for causal inference is at the heart of a "credibility revolution" in empirical research. We will cover the design of true randomized experiments and contrast them to simulations and quasi-experiments, where part of the sample is "treated" in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the kinds of causal inferences one can and cannot draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods are often adapted to a particular study. Some of the methods are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to use with messy, real-world datasets and limited sample sizes. Each day will include with a Stata "workshop" to illustrate selected methods with real data and Stata code.
Statistical software We will use Stata, and will provide attendees with a temporary Stata14 license. Versions 10, 11, 12, and 13 should be fine for most purposes (but you may need to "downconvert" datasets. Each day will conclude with a Stata "workshop" where we will illustrate selected methods with real data and Stata code.
Target audience Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), medicine, sociology, education, psychology, etc. – anywhere that causal inference is important.
Minimum prior knowledge We will assume knowledge, at the level of an upper-level college econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training. For recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. The faculty are listed in order of appearance
- Donald Rubin (Harvard University, Department of Statistics) Rubin is John L. Loeb Professor of Statistics, Harvard University. His work on the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia: http://en.wikipedia.org/wiki/Donald_Rubin
- Stephen L. Morgan (Johns Hopkins, Sociology and Education) Stephen Morgan is Bloomberg Distinguished Professor of Sociology and Education, Johns Hopkins University. He is a co-author (with Christopher Winship of Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed. 2015). Principal research interests: inequality, education, and demography.
- Jens Hainmueller is Associate Professor in the Stanford Political Science Department. He also holds a courtesy appointment in the Stanford Graduate School of Business. His research interests include statistical methods, political economy, and political behavior.
Conference Organizers
- Bernard Black (Northwestern University) Nicholas J. Chabraja Professor at Northwestern University School of Law, with a secondary appointment at Kellogg School of Management, Department of Finance. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN
- Mathew McCubbins (Duke University) Professor of Political Science and Law at Duke University, with positions in the Law School and the Political Science Department, and director of the Center for Law and Democracy. Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decision-making; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN
Workshop Schedule
General schedule: Breakfast available at 8:30. “Lecture” sessions will run roughly 9:00-noon, lunch noon-1:00, sessions 1:00-3:00. “Stata sessions”: 3:15-4:15. All times except starting times are approximate. Please plan to arrive Sunday evening. Variations for particular days are indicated below. Return travel: The Friday afternoon sessions will end by 5:00, a return flight leaving 7:00 or later from O’Hare is safe; a bit earlier from Midway.
Monday, July 13 (Donald Rubin)
Registration and breakfast: 8:00
Introduction to Modern Methods for Causal Inference Overview of causal inference and the Rubin “potential outcomes” causal model. The “gold standard” of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Causal inference as a missing data problem, and imputation of missing potential outcomes.
Tuesday-Wednesday, July 14-15 (Stephen Morgan)
Designs for “Pure” Observational Studies
Designs for “Pure” Observational Studies Selection [only] on observables and common support assumptions. Subclassification, matching, reweighting, and regression estimators of average treatment effects. Propensity score methods. What to match on: an introduction to directed acyclic graphs.
Instrumental variable methods
Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.
Tuesday-Wednesday, July 16-17 (Thursday-Friday, July 16-17 (Jens Hainmueller)
Panel Data and Difference-in-Differences
Panel Data and Difference-in-Differences Panel data methods: pooled OLS, random effects, correlated random effects, and fixed effects. Simple two-period DiD. The core “parallel changes” assumption. Testing this assumption. Leads and lags and distributed lag models. When does a design with unit fixed effects become DiD? Accommodating covariates. Triple differences. Robust and clustered standard errors.
Regression Discontinuity
Regression Discontinuity (Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; testing for covariate balance and manipulation of the threshold; discontinuities as substitutes for true randomization and sources of convincing instruments.
Friday afternoon: Feedback on your own research
Attendees will present their own research design questions from current work in breakout sessions and receive feedback on research design. Session leaders: Bernie Black, Mat McCubbins, Jens Hainmueller. Parallel sessions as needed to meet demand.
2014 Fifth Annual Advanced Causal Inference Workshop
We would like to invite you to attend our second "advanced" workshop on Research Design for Causal Inference. The workshop builds on our "main" workshop. The workshop is sponsored by Northwestern Law School, the Center for the Study of Democracy and the Rule of Law at Duke Law School, and the Society for Empirical Legal Studies.
Wednesday-Friday, August 13-15, 2014, at Duke University School of Law, Durham, NC
Our regular "Main" Workshop on Research Design for Causal Inference will be held this year on July 7-11, 2014, at Northwestern Law School.
Workshop Overview
The advanced causal inference workshop seeks to provide an in-depth discussion of selected topics that are beyond what we can cover in the main workshop. Principal topics for 2014 include: Day 1: Choosing estimands (the science), and how choice of estimand affects research design. Principal stratification methods (a little known, but very powerful extension of the always taker/never-taker/complier/defier categories developed in “causal IV”); advanced matching methods; multiple imputation of missing potential outcomes. Day 2: Simulation studies; bootstrap methods; advanced topics in regression discontinuity design. Day 3: Causal inference with panel data. Topics will include handling treatment heterogeneity, handling time dynamics, synthetic controls, marginal structural models, and standard errors.
Target Audience for Advanced Workshop
Our target audience is empirical researchers who are reasonably familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge. We will assume familiarity with the potential outcomes notation, randomization inference, difference-in-differences, regression discontinuity, panel data, and instrumental variable designs, but will not assume expertise in any of these areas.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
- Donald B. Rubin (Harvard University)
John L. Loeb Professor of Statistics, Harvard University. His work on what today is often called the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia - Jonathan N. Katz (California Institute of Technology)
Katz is Kay Sugahara Professor of Social Sciences and Statistics at Caltech. Co-editor: Political Analysis. Principal research interests: American politics, political methodology; formal political theory. Web page with link to CV: Web page with link to CV: http://jkatz.caltech.edu/. - Justin McCrary (University of California, Berkeley, Law School)
Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies. Papers on SSRN
Conference Organizers
- Bernard Black (Northwestern University)
Nicholas J. Chabraja Professor at Northwestern University School of Law, with a secondary appointment at Kellogg School of Management, Department of Finance. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studiesPapers on SSRN - Mathew McCubbins (Duke University)
Professor of Political Science and Law at Duke University, with positions in the Law School and the Political Science Department, and director of the Center for Law and Democracy. Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decision-making; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN
Workshop Schedule
General schedule: Breakfast available at 8:30. “Lecture” sessions will run roughly 9:00-noon, lunch and lunch talk noon-1:45, sessions 1:45-4:45. All times except starting times are approximate. Please plan to arrive Tuesday evening. Variations for particular days are indicated below.
Registration and welcome breakfast: August 13, 8:00 a.m.
Wednesday August 13 (Don Rubin)
Principal Stratification, Flexible Matching Methods, and Multiple Imputation
Choosing estimands (the science). Implications of choice of estimand for choice of method. Principal stratification. Flexible matching methods. Multiple imputation of missing potential outcomes. And whatever else Don thinks he should cover, in the allotted time.
Lunch talk (Justin McCrary)
Wednesday reception 4:30-6:00p
Thursday August 14 (Justin McCrary)
Breakfast available from 8:30
Simulation and Bootstrapping
Conducting simulation studies. Inference and testing using the bootstrap, including adapting bootstrap methods to your research design. Choosing among balancing methods: Matching, reweighting, and regression adjustment. Topics in regression discontinuity design: nonparametric estimation; Local linear regression and density estimation; choosing bandwidth and assessing sensitivity to bandwidth choice.
Lunch Talk (Elizabeth Zell, Centers for Disease Control): Impact of pneumococcal vaccine (PCV13) introduction (involving model-based imputation of missing potential outcomes).
Friday August 15 (Jonathan Katz)
Breakfast available from 8:30
Causal inference with panel data
Selected topics in causal inference with panel data, including time-series-cross-sectional (TSCS) data. Topics will include issues of unit heterogeneity, specification of dynamics, synthetic matching, and marginal structural models, and which standard errors to use.
Lunch talk: Advice from a journal editor on what to do (and not do) (Jonathan Katz is co-editor-in-chief of Political Analysis).
2014 Fifth Annual Main Causal Inference Workshop
We would like to invite you to attend the fifth annual workshop on Research Design for Causal Inference, sponsored by Northwestern University, Duke University, and the Society for Empirical Legal Studies.
Monday-Friday, July 7-11, 2014, at Northwestern Law School, 375 East Chicago Avenue, Chicago, IL
Registration is limited to 100 participants. We filled up quickly last year, so please register soon.
An Advanced Workshop on Research Design for Causal Inference will be held this year on August 13-15, 2014 at Duke University.
Workshop Overview
Research design for causal inference is at the heart of a "credibility revolution" in empirical research. We will cover the design of true randomized experiments and contrast them to simulations and quasi-experiments, where part of the sample is "treated" in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the kinds of causal inferences one can and cannot draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses survey a variety of methods. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods are often adapted to a particular study. Some of the methods are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to use with messy, real-world datasets and limited sample sizes. Each day will include with a Stata “workshop” to illustrate selected methods with real data and Stata code.
Statistical software
We will use Stata, and will provide attendees with a temporary Stata13 license. Versions 10, 11, and 12 should be fine for most purposes (but you may need to “downconvert” datasets. Each day will conclude with a Stata “workshop” where we will illustrate selected methods with real data and Stata code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), medicine, sociology, education, psychology, etc. – indeed anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables. Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training. Even for recent PhD’s, there will be much that you don’t know, or don’t know as well as you should.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training. For recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
- Justin McCrary (University of California, Berkeley)
Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies. Papers on SSRN - Alberto Abadie (Harvard University)
Professor of Public Policy at the Kennedy School of Government at Harvard University. Principal research interests: econometrics; program evaluation. Papers on SSRN - Jens Hainmueller (Stanford University, Political Science) is an Associate Professor in the Department of Political Science at Stanford University. He also holds a courtesy appointment in the Stanford Graduate School of Business. http://www.stanford.edu/~jhain//
Conference Organizers
- Bernard Black (Northwestern University)
Nicholas J. Chabraja Professor at Northwestern University School of Law, with a secondary appointment at Kellogg School of Management, Department of Finance. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies Papers on SSRN - Mathew McCubbins (Duke University)
Professor of Political Science and Law at Duke University, with positions in the Law School and the Political Science Department, and director of the Center for Law and Democracy. Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decision-making; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN
Workshop Schedule
General schedule
Breakfast available at 8:30. “Lecture” sessions will run roughly 9:00-noon, lunch noon-1:00, sessions 1:00-3:00. “Stata sessions”: 3:15-4:15. All times except starting times are approximate. Please plan to arrive Sunday evening. Variations for particular days are indicated below. Return travel: The Friday afternoon sessions will end by 5:00, a return flight leaving 7:00 or later from O’Hare is safe; a bit earlier from Midway.
Monday-Tuesday, July 7-8 (Justin McCrary)
Registration and breakfast: 8:00
Introduction to Modern Methods for Causal Inference
Overview of causal inference and the Rubin “potential outcomes” causal model. The “gold standard” of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Causal inference as a missing data problem, and imputation of missing potential outcomes.
Monday reception
4:30-6:00: In central courtyard if weather permits.
Instrumental variable and regression discontinuity methods
Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.
(Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; testing for covariate balance and manipulation of the threshold; discontinuities as substitutes for true randomization and sources of convincing instruments.
Wednesday, July 9 - Thursday morning July 10 (Alberto Abadie)
Observational Studies: Selection on observables
Selection on observables and common support assumptions. Subclassification, matching, and regression estimators of average treatment effects. Propensity score methods: matching and weighting. What to match on: a brief introduction to directed acyclic graphs.
Standard Errors
Robust and clustered standard errors. The bootstrap.
Thursday lunch talk
Bernie Black, Bloopers: How Smart People Get Causal Inference Wrong. (Afternoon session will be pushed back and will end around 5:00)
Thursday afternoon, July 10 - Friday morning, July 11 (Jens Hainmueller)
Difference-in-Differences, Panel Data, and Synthetic Controls
Simple two-period DiD; the “parallel changes” assumption. Leads and lags and distributed lag models. Accommodating covariates. Triple differences. Panel data methods. Synthetic controls.
Friday lecture sessions will end at noon.
Friday afternoon: Feedback on your own research
Attendees will present their own research design questions from current work in breakout sessions and receive feedback on research design. Session leaders: Bernie Black, Mat McCubbins, Jens Hainmueller. Parallel sessions as needed to meet attendee demand.
2013 Fourth Annual Main Causal Inference Workshop
We would like to invite you to attend the fourth annual workshop on Research Design for Causal Inference, sponsored by Northwestern University, the University of Southern California, and the Society for Empirical Legal Studies.
Monday-Friday, June 24-28, 2013, at Northwestern Law School, Chicago, IL
An Advanced Workshop on Research Design for Causal Inference will be held this year on August 12-14, 2013.
Workshop Overview
Research design for causal inference is at the heart of a "credibility revolution" in empirical research. We will cover the design of true randomized experiments and contrast them to simulations and quasi-experiments, where part of the sample is "treated" in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the kinds of causal inferences one can and cannot draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods reflect the goal and are often adapted to the needs of a particular study. Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes.
Statistical software
We will use Stata, and will provide attendees with a temporary Stata12 license. Versions 10 and 11 should be fine for most purposes (but you may need to “downconvert” datasets. Each day will conclude with a Stata “workshop” where we will illustrate selected methods with real data and Stata code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), sociology, education, psychology, etc. – indeed anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or applied statistics course, of how to run multivariate regressions, including OLS, logit, and probit; familiarity with basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training. For recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
- Guido Imbens (Stanford University)
Professor of Economics at Stanford University, Graduate School of Business. Principal research interests: econometric theory, applied econometrics, labor economics. Coauthor with Donald Rubin of Causal Inference in Statistics and Social Sciences (draft 2012). Papers on SSRN - Justin McCrary (University of California, Berkeley)
Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies. Papers on SSRN - Alberto Abadie (Harvard University)
Professor of Public Policy at the Kennedy School of Government at Harvard University. Principal research interests: econometrics; program evaluation. Papers on SSRN
Conference Organizers
- Bernard Black (Northwestern University, Law and Kellogg School of Management)
Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN - Mathew McCubbins (University of Southern California)
Provost Professor of Business, Law and Political Economy at University of Southern California, with positions in the Marshall School of Business, the Gould School of Law, and the Department of Political Science. Principal research interests: legislative organization; communication, learning and decision-making; research design; network economics. Papers on SSRN
Workshop Schedule
Monday, June 24 (Guido Imbens)
Introduction to Modern Methods for Causal Inference; Analysis of Randomized Experiments
Overview of causal inference and the Rubin "potential outcomes" causal model. The "gold standard" of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Causal inference as a missing data problem, and imputation of missing potential outcomes.
Reading: Imbens and Rubin, Causal Inference in Statistics and Social Sciences, chapters 1-8 (chapter 2 is background and can be skipped).
Tuesday, June 25 (Guido Imbens)
Pure Observational Studies: Matching and Propensity Score Methods
The core, untestable "unconfoundedness" or "selection on observables" assumption. The need for overlap between treated and control units. Propensity scores, matching methods, blocking on the propensity score. Trimming to deal with lack of overlap. Average treatment effects on the treated (ATT), the controls (ATC) and the whole sample (ATE). Near-equivalence of matching and reweighting.
Reading: Imbens and Rubin, Causal Inference in Statistics and Social Sciences, chapters 12-21, 23
Wednesday, June 26 (Justin McCrary)
Instrumental variable and regression discontinuity methods
Instrumental variables (IV), including (i) the core (untestable) need to satisfy the "only through" exclusion restriction, (ii) heterogeneous treatment effects; (iii) randomized trials or quasi-experiments with noncompliance.
Reading: Angrist and Pischke, Mostly Harmless Econometrics, chaps. 2, 4
(Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; need to test (not just assume) covariate balance; discontinuities as substitutes for true randomization and as sources of convincing instruments.
Readings: Justin McCrary and Heather Royer (2011), The Effect of Female Education on Fertility and Infant Health: Evidence from School Entry Laws Using Exact Date of Birth", 101 American Economic Review 158–95.
David S. Lee (2008), Randomized Experiments from Non-random Selection in U.S. House Elections, 142 Journal of Econometrics 675-697.
Thursday, June 27 (Alberto Abadie)
Difference-in-Differences (DiD), Panel Data, and Synthetic Controls
Difference-in-Differences: Simple two-period DiD; the “parallel changes” assumption. Accommodating covariates. Triple differences. Panel data methods. Synthetic controls.
Readings: Abadie, Alberto, Alexis Diamond and Jens Hainmueller (2010), Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program, 105 Journal of the American Statistical Association 493-505.
Angrist, Joshua, and Jorn-Steffen Pischke (2009), Mostly Harmless Econometrics: An Empiricist’s Companion ch. 5.
Recommended (but we know it’s another book to buy): Cameron, A. Colin, and Pravin Trivedi (2005), Microeconometrics, Methods and Applications chs. 21-23
Thursday post-lunch talk (Bernie Black)
Bloopers
Examples drawn from different areas of how to get causal inference wrong.
Friday morning, June 28 (Alberto Abadie)
Further Topics: Standard Errors, Directed Acyclic Graphs, Event Studies
Standard errors: ordinary, robust, and clustered standard errors. The bootstrap. Introduction to directed acyclic graphs. Event studies.
Readings: Abadie, Alberto, and Javier Gardeazabal (2003), The Economic Costs of Conflict: A Case Study of the Basque Country, 93 American Economic Review 113-132.
Angrist, Joshua, and Jorn-Steffen Pischke (2009), Mostly Harmless Econometrics: An Empiricist’s Companion ch. 8.
Recommended: Cameron and Trivedi (2005), Microeconometrics, Methods and Applications ch. 11.
Morgan, Steven, and Christopher Winship (2007) Counterfactuals and Causal Inference: Methods and Principles for Social Research ch. 3.
Friday afternoon
Feedback on your own research
Attendees will have an opportunity to present their own research design questions from current work in breakout sessions. Goal: obtain feedback on research design; not present results from a complete paper. Session leaders: Bernie Black, Mat McCubbins, Alberto Abadie. Parallel sessions as needed to meet demand) (15 min to present, 15 min discussion).
2013 Advanced Causal Inference Workshop
We would like to invite you to attend our first "advanced" workshop on Research Design for Causal Inference. The workshop builds on our "main" workshop. The workshop is sponsored by Northwestern Law School, the Center for the Study of Democracy and the Rule of Law at Duke Law School, and the Society for Empirical Legal Studies.
Monday-Wednesday, August 12-14, 2013, at Northwestern Law School, Chicago, IL
Our regular "Main" Workshop on Research Design for Causal Inference will be held this year on June 24-28, 2013.
Workshop Overview
There continues to be rapid change in the "research frontier" for causal inference, and in what constitutes best practice (normally somewhat inside the frontier). The goal for the advanced workshop is to provide an in-depth discussion of selected topics in causal inference that won’t fit in the main workshop. Our target audience is empirical researchers (faculty and graduate students) who are familiar with the fundamentals of causal inference (from our main workshop, a graduate-level causal inference class, or their own research), who want to extend their knowledge. Most methods are not field-specific. Thus, we expect the workshop to be suitable for researchers in a wide variety of areas, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), medicine, sociology, education, psychology, etc. – indeed anywhere that careful causal inference is important.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
- Donald B. Rubin (Harvard University) John L. Loeb Professor of Statistics, Harvard University. His work on what today is often called the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia
- Ben Hansen (University of Michigan) Associate Professor of Statistics, University of Michigan, and Faculty Associate at the University of Michigan Institute for Social Research. Principal research interests: causal inference in observational studies, design-based inference for randomized experiments, statistical computing; applications to health outcomes research, social epidemiology, political science and education evaluation. CV (pdf)
- Justin McCrary (University of California, Berkeley) Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies. Papers on SSRN
Conference Organizers
- Bernard Black (Northwestern University, Law and Kellogg School of Management) Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN
- Mathew McCubbins (Duke University) Professor of Political Science and Law. Principal research interests: legislative organization; communication, learning and decision-making; research design; network economics. Papers on SSRN
Workshop Schedule
Monday, August 12 (Don Rubin) Choosing estimands (the science). Implications of choice of estimand for choice of method. Principal stratification. Flexible matching methods. Multiple imputation of missing potential outcomes.
Suggested readings (all are posted):
Imbens, Guido, and Donald Rubin (2013), Causal Inference in Statistics and Social Sciences, chs. xx-yy(BSB note: We posted the full draft book).
Rubin, Donald B. (2008), For Objective Causal Inference, Design Trumps Analysis, 2 Annals of Applied Statistics 808-840.
On estimands:
Rubin, Donald (2005), Causal Inference Using Potential Outcomes: Design, Modeling, Decisions, 100 Journal of the American Statistical Association 322-331.
On principal stratification:
Comment: Principal stratification was first used (although the term was developed later) in the context of instrumental variable estimates of “local average treatment effects” for treatment with non-compliance. See Angrist, Joshua, Guido Imbens, and Donald Rubin (1996), 91 Journal of the American Statistical Association 444-455. The groups relevant for this analysis (always takers, never takers, compliers, and defiers) can be understood as principal strata. If you are not familiar with this use of principal stratification, please read Imbens and Rubin chs. 24-25.
Meulli, Fabrizia, and Donald Rubin (2003), Assumptions Allowing the Estimation of Direct Causal Effects, 112 Journal of Econometrics 79-87. [Note: we have not posted the long, complex paper by Peter Adams et al. that Meulli and Rubin are commenting on; you need to know only that they estimate something they call a “direct” causal effect of wealth on mortality by conditioning on health.]
Imbens, Guido, and Donald Rubin (2012), Causal Inference in Statistics and Social Sciences, ch. 28 on Principal Stratification (BSB note: This chapter is not included in the 2013 version of this book.)
Tuesday, August 13 (Ben Hansen) Advanced matching. This session focuses on the use of propensity-score based, optimal matching methods to reliably secure covariate balance and, with additional assumptions, consistency and robustness of causal effect estimation. Methods are demonstrated with code and exercises in R. Propensity score matching is the best known of a number of techniques that play roles both in study design and in statistical analysis; the session includes a comparative discussion of alternative matching and weighting methods.
Suggested readings (all are posted):
Rosenbaum, Paul R. (2009), Design of Observational Studies, ch. 13 (Matching in R); ch. 18 (After Matching, Before Analysis) (BSB note: We posted these chapters, but you ought to buy the whole book. When I did so though Northwestern, it came with a pdf version, which I find very useful.)
Hosman, Ben Hansen and Paul Holland (2010), The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder, 4 Annals of Applied Statistics 849-870, esp. §§ 1, 2.1, and 4.3.
Imbens and Rubin (12013), ch. 23 (Sensitivity Analysis and Bounds).
Wednesday, August 14 (Justin McCrary) Conducting simulation studies. Generalized method of moments (GMM) as a tool for estimating treatment effects and standard errors, including adjusting standard errors for two-step estimation (e.g., reweighting). Inference and testing using the bootstrap. Topics in regression discontinuity design: nonparametric estimation; Local linear regression and density estimation; choosing bandwidth and assessing sensitivity to bandwidth choice.
Suggested readings (all are posted):
Cameron, A. Colin, and Praveen Trivedi (2010), Microeconometrics Using Stata, chapter 4 and chapter 13 (BSB note: We posted these chapters).
An additional source for simulation in Stata is Adkins, Lee, and Mary Gade (2012), Monte Carlo Experiments Using Stata: A Primer with Examples.
Hansen, Bruce (2013), Econometrics, chs. 10-12, 14 (BSB note: We posted the full book).
2012 Third Annual Main Causal Inference Workshop
Workshop Overview
Research design for causal inference is at the heart of a "credibility revolution" in empirical research. We will cover the design of true randomized experiments and contrast them to simulations and quasi-experiments, where part of the sample is "treated" in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the kinds of causal inferences one can and cannot draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods reflect the goal and are often adapted to the needs of a particular study. Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes.
Each day will conclude with a Stata "workshop" where we will illustrate selected methods with real data and Stata code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), sociology, education, psychology, etc. - indeed anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or applied statistics course, of how to run multivariate regressions, including OLS, logit, and probit; familiarity with basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables are and how they are used.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable even if more limited training. Especially for recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. Click on the links below and see for yourself.
- Donald B. Rubin (Harvard University) is John L. Loeb Professor of Statistics, Harvard University. His work on what today is often called the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia
- Justin McCrary (University of California, Berkeley) is Professor of Law, University of California, Berkeley. Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies.
- Alberto Abadie (Harvard University) is Professor of Public Policy at the Kennedy School of Government at Harvard University. Principal research interests: econometrics; program evaluation. Papers on SSRN
- Jens Hainmueller (MIT, Political Science) is Associate Professor at the Massachusetts Institute of Technology. Principal research interests: applied statistics, immigration, political economy, program evaluation. Web page with link to CV and synthetic controls software.
Conference Organizers
- Bernard Black (Northwestern University, Law and Kellogg School of Management) is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN
- Mathew McCubbins (University of Southern California) is Provost Professor of Business, Law and Political Economy at University of Southern California, with positions in the Marshall School of Business, the Gould School of Law, and the Department of Political Science. Principal research interests: legislative organization; communication, learning and decision-making; research design; network economics. Papers on SSRN
Workshop Schedule
Monday, August 6 (Don Rubin)
Overview of causal inference and the Rubin "potential outcomes" causal model. The "gold standard" of a randomized experiment. Treatment and control groups, and the core role of the assignment (to treatment) mechanism. Multiple imputation of missing potential outcomes.
Tuesday, August 7 (Justin McCrary)
Instrumental variables (IV), including (i) the core (untestable) need to satisfy the "only through" exclusion restriction, (ii) heterogeneous treatment effects; (iii) randomized trials or quasi-experiments with noncompliance.
(Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; need to test (not just assume) covariate balance; discontinuities as substitutes for true randomization and as sources of convincing instruments.
Wednesday, August 8 (Alberto Abadie)
Selection (only) on observables, matching and subclassification, comparing matching to regression, propensity score methods, handling poorly matched observations.
Thursday, August 9 (Alberto Abadie)
Matching methods continued, combining matching with DiD, synthetic controls, standard error issues (robust and clustered standard errors, bootstrapping).
Thursday post-lunch talk (Bernie Black): Bloopers
Examples drawn from different areas of how to get causal inference wrong.
Friday morning, August 10 (Jens Hainmueller)
Panel data: Fixed and random effects, and the choice between then. DiD with multiple before and after time periods. Standard errors - what do you cluster on?
Friday afternoon: Feedback on your own research
Attendees will have an opportunity to present their own research questions from current work in breakout sessions and receive group feedback. Session leaders: Bernie Black, Jens Hainmueller, Mat McCubbins,(parallel sessions as needed to meet demand) (15 min to present, 15 min discussion)
2011 Second Annual Causal Inference: Frequentist Methods Workshop
Dates and Location: Monday - Friday, August 15-19, 2011, at Northwestern Law School, Chicago, IL
Overview of the Causal Inference Workshop
Research design for causal inference is at the heart of a "credibility revolution" in empirical research. We will cover the design of true randomized experiments and contrast them to simulations and quasi-experiments, where part of the sample is "treated" in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the kinds of causal inferences one can and cannot draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods reflect the goal and are often adapted to the needs of a particular study. Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes.
Each day will conclude with a Stata "workshop" where we will illustrate selected methods with real data and Stata code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), sociology, education, psychology, etc. - indeed anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or applied statistics course, of how to run multivariate regressions, including OLS, logit, and probit; familiarity with basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables are and how they are used.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable even if more limited training. Especially for recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. Click on the links below and see for yourself.
- Alberto Abadie (Harvard University) is Professor of Public Policy at the Kennedy School of Government at Harvard University. Principal research interests: econometrics; program evaluation. Papers on SSRN
- Joshua Angrist (MIT) is Ford Professor of Economics at MIT. Principal research interests: labor economics; econometrics. Author of Joshua Angrist and Jorn-Steffen Pischke, Mostly Harmless Econometrics: An Empiricist's Companion (2009). Mostly Harmless Econometrics blog. Papers on SSRN
- Theodore Eisenberg (Cornell University) is Henry Allen Mark Professor of Law and Adjunct Professor of Statistical Sciences at Cornell University, and the founding editor of the Journal of Empirical Legal Studies. Principal research interest: empirical studies of the legal system. Papers on SSRN
- Daniel E. Ho (Stanford University) is Professor of Law and Robert E. Paradise Faculty Fellow for Excellence in Teaching and Research at Stanford Law School. Principal research interests: empirical legal studies; administrative law; judicial behavior; antidiscrimination law. Papers on SSRN
- Guido Imbens (Harvard University) is Professor of Economics at Harvard. Principal research interests: econometric theory, applied econometrics, labor economics. Coauthor with Donald Rubin of Causal Inference in Statistics and Social Sciences (forthcoming 2011). Papers on SSRN
Conference Organizers
- Bernard Black (Northwestern University, Law and Kellogg School of Management) is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management, Department of Finance. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN
- Mathew McCubbins (University of Southern California) is Provost Professor of Business, Law and Political Economy at University of Southern California, with positions in the Marshall School of Business, the Gould School of Law, and the Department of Political Science. Principal research interests: legislative organization; communication, learning and decision-making; research design; network economics. Papers on SSRN
Workshop Schedule
Breakfast will be available each morning from 8:30 a.m.
A registration table will be open Monday beginning 8:00 a.m.
- Monday, August 15 (Joshua Angrist)
- Introduction by Bernie Black: Lessons We Hope You'll Learn This Week
- Overview of regression analysis, and when it does and does not permit causal inference
Angrist and Pischke, Mostly Harmless Econometrics, chaps 1-3 (mostly 3) - Introduction to instrumental variables with constant treatment effects
Angrist and Pischke, Mostly Harmless Econometrics, chap 4
Monday evening
- Reception for attendees from 5:00-6:30 p.m.
- Tuesday, August 16 (Joshua Angrist)
- Instrumental variables (i) with heterogeneous treatment effects; and (ii) in randomized trials or quasi-experiments with noncompliance
Angrist and Pischke, Mostly Harmless Econometrics, chap 4 - Difference-in-differences (DiD) analysis
Angrist and Pischke, Mostly Harmless Econometrics, chap 5
- Instrumental variables (i) with heterogeneous treatment effects; and (ii) in randomized trials or quasi-experiments with noncompliance
- Wednesday, August 17 (Guido Imbens)
- Research design in observational studies
- Methods for improving matching and covariate balance
- Thursday, August 18 (Alberto Abadie)
- Matching methods; choice among matching, regression, or a combination
- Synthetic controls
- Standard error issues (robust and clustered standard errors, bootstrapping)
Thursday post-lunch talk (Bernie Black)
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- Bloopers: Examples, drawn from different areas, of how to get causal inference wrong.
- Friday, August 19 - morning (Daniel Ho)
- Regression discontinuity: sharp and fuzzy designs; bandwidth choice; parametric and nonparametric options; covariate balance.
Friday post-lunch talk (Ted Eisenberg)
- Strengths and Weaknesses in Empirical Studies: Observations of a Journal Editor
Friday afternoon
- Feedback on your own research: Attendees will have an opportunity to present their own research questions from current work in breakout sessions (session leaders: Bernie Black, Ted Eisenberg, Dan Ho, Mat McCubbins)
- Present your own research design issues; get group feedback (parallel sessions as needed to meet demand) (15 min to present, 15 min discussion)
Workshop Materials
Workshop materials are available only to participants.
Login
The username and password have been sent to all participants. If you can't locate them, please e-mail causalinference@law.northwestern.edu
2010 First Annual Causal Inference: Frequentist Methods Workshop
Dates and Location: Monday - Friday, August 16-20, 2010, at Northwestern Law
Overview: Research design for causal inference is at the heart of a "credibility revolution" in empirical research in the last 15 years that spans many fields. We will cover the design of true randomized experiments and contrast them to simulations and to quasi-experiments, where part of the sample is "treated" in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will carefully describe the kinds of causal inferences one can and cannot draw from a research design, various threats to valid inference, and research designs commonly used to mitigate those threats.
Most empirical methods courses begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods reflect the goal and are often adapted to the needs of a particular study. Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes.
Target audience: Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, most business-school areas (finance, accounting, marketing, etc), sociology, education, psychology – indeed anywhere that causal inference is important.
Minimum prior knowledge: We will assume knowledge, at the level of an upper-level college econometrics or applied statistics course, of how to run simple regressions, including OLS, logit, and probit; familiarity with basic probability and statistics including conditional and compound probabilities; confidence intervals, t-statistics and standard errors.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable even if more limited training. Especially for recent PhDs, there will be overlap with what you already know, but also much that you don't know, or don't know as well as you need to.
Teaching Faculty
We are fortunate to have recruited truly outstanding experts in causal research design to teach the workshop sessions.
- Nathaniel Beck (New York University)
Nathaniel Beck is Professor of Politics at NYU. Principal research interests: political methodology (empirical methods applied to political science research), especially research design, time-series and time-series cross section analysis; comparative political economy. - Thomas Cook (Northwestern University)
Thomas Cook is Joan and Sarepta Harrison Chair in Ethics and Justice, Professor of Sociology, Psychology, Education and Social Policy, and Faculty Fellow, Institute for Policy Research, at Northwestern University. He is a leading scholar on experimental and quasi-experimental design and regression discontinuity and a coauthor of a classic text, Shadish, Cook, and Campbell, Experimental and Quasi-Experimental Designs for Generalized Causal Inference (2002). - Guido Imbens (Harvard University)
Guido Imbens is Professor of Economics at Harvard. Principal research interests: econometric theory, applied econometrics, labor economics. Coauthor with Donald Rubin of Causal Inference in Statistics and Social Sciences (forthcoming 2010). Papers on SSRN - Jonathan N. Katz (California Institute of Technology)
Jonathan Katz is Professor of Social Sciences and Statistics and Chair of the Division of the Humanities and Social Sciences at Caltech. Principal research interests: American politics, political methodology; formal political theory. - Kevin Quinn (University of California, Berkeley)
Kevin Quinn is Professor of Law at University of California, Berkeley, School of Law. Principal research interests: empirical methods in law and political science. Papers on SSRN - Donald Rubin (Harvard University)
Donald Rubin is John L. Loeb Professor of Statistics at Harvard University. His work on what today is often called the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics. Wikipedia entry
Conference Organizers
- Bernard Black (Northwestern University Law and Kellogg School of Management)
Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management, Department of Finance. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN - Mathew McCubbins (University of Southern California)
Mat McCubbins is Provost Professor of Business, Law and Political Economy at University of Southern California, with positions in the Marshall School of Business, the Gould School of Law, and the Department of Political Science. Principal research interests: legislative organization; communication, learning and decision-making; research design; network economics. Papers on SSRN
Workshop Outline
- Day 1: Introduction to Research Design for Causal Inference [Donald Rubin]
- Introduction [Mathew McCubbins]
- Morning: brass standard: Ordinary regression; its uses and limits
- Afternoon: gold standard: randomized controlled trial with predefined goal and endpoint
- Day 2: Estimating Treatment Effects [Guido Imbens]
- Morning: Research design to produce "ignorable" assignment to treatment or control group, conditioned on observable controls ("unconfounded" assignment)
- Afternoon: Addressing non-ignorable assignment to treatment group
- Day 3: Panel Data [Jonathan Katz]
- Morning: Basic panel data designs
- Post-lunch lecture: The importance of choice of control variables in observational studies [Thomas Cook]
- Afternoon: difference-in-differences; standard errors; wide panels; triple difference designs
- Day 4: Instrumental Variables and Selection Bias [Nathaniel Beck]
- Morning: The basics of instrumental variables
- Afternoon: weak instruments; simultaneous equations; multiple instruments (overidentification); tests for instrument validity. Selection bias and ways to address it.
- Day 5 morning: Interpreting Treatment Effects [Kevin Quinn]
- What inferences can one draw from a research design for a heterogeneous population: sample versus population average treatment effects (ATE); local average treatment effects (LATE); average treatment effect on the treated (ATET); intent to treat effect (ITT), etc.
- Post-lunch lecture: Bloopers [Bernie Black].
Examples, drawn from different areas, of how to get causal inference wrong. - Day 5 afternoon: Feedback on your own research [led by Black and McCubbins]
present your own research design issues; get group feedback (parallel sessions as needed to meet demand) (15 min to present, 15 min discussion)
Workshop Materials
Workshop materials are available only to participants.
To obtain the username and password, please e-mail causalinference@law.northwestern.edu
2011 Causal Inference: Bayesian Methods Workshop
Dates and Location: July 11-13, 2011, at Northwestern Law School, Chicago, IL
Overview of the Bayesian Causal Inference Workshop
Research design for causal inference is at the heart of a "credibility revolution" in empirical research. Credible causal inference often requires researchers not to rely on the linearity and normality assumptions underlying classical regression. Bayesian imputation and simulation methods provide many of the analytic tools for doing so. We will cover Bayesian methods for research design and analysis for true randomized experiments, as well as for quasi-experiments where part of the sample is "treated" in some way, the control group is drawn from the rest of the sample, but the researcher controls neither the assignment of units to treatment nor administration of the treatment. These analytic methods include multiple imputation of missing "potential outcomes", using Markov chain Monte Carlo (MCMC) simulations, including Gibbs sample, and other flexible model specifications.
Most empirical methods courses begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference and the centrality of research design, and discuss how Bayesian methods allow research designs that better achieve that goal.
The workshop will include an introduction to Winbugs, the principal public domain Bayesian inference software. We will illustrate selected methods with real data and Winbugs code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), sociology, education, psychology, etc. - indeed anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or applied statistics course, of how to run multivariate regressions, including OLS, logit, and probit; familiarity with basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables are and how they are used.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable even if more limited training. Especially for recent PhD's, there will be overlap with what you already know, but much that you don't know, or don't know as well as you should.
Teaching Faculty
We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
- Donald B. Rubin (Harvard University) is John L. Loeb Professor of Statistics, Harvard University. His work on what today is often called the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia
- Jeff Gill (Washington University in St. Louis) is Director of the Center for Applied Statistics, Professor of Political Science and Professor of Mathematics at Washington University in St. Louis, and the author of Bayesian Methods: A Social and Behavioral Approach (second edition 2007). Principal research interests: political methodology, Bayesian research methods; statistical computing. Wikipedia
Conference Organizers
- Bernard Black (Northwestern University, Law and Kellogg School of Management) is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management. Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies. Papers on SSRN
- Mathew McCubbins (University of Southern California) is Provost Professor of Business, Law and Political Economy at University of Southern California, with positions in the Marshall School of Business, the Gould School of Law, and the Department of Political Science. Principal research interests: legislative organization; communication, learning and decision-making; research design; network economics. Papers on SSRN
Workshop Schedule
The workshop will run for three full days from 9:00 a.m.-4:30 p.m., plus a reception on Monday from 4:30 p.m.-6:00 p.m. There will be roughly six 50-min lectures each day, plus a lunch break from roughly 12:15 p.m.-1:30 p.m. The starting time is fixed, all else is approximate.
- Monday July 11 (Don Rubin)
The "potential outcomes (with assignment mechanism) or "Rubin causal model" approach to causal inference. Understanding the assignment mechanism. Estimating treatment effects using multiple imputation of the missing outcomes for both treated and control units. Comparison of multiple imputation to regression methods. Research design for credible causal inference. Applications to randomized experiments and to "natural" or quasi-experiments with "unconfounded" assignment of units to treatment. - Tuesday July 12 (Jeff Gill)
The basics of Bayesian inference, including the probabilistic treatment of uncertainty, prior and posterior distributions, updating knowledge, and describing results. Comparison of Bayesian to frequentist approaches. Bayesian approaches to construction and assessment of linear and nonlinear regression-style models. - Wednesday, July 13 (Jeff Gill)
Bayesian stochastic simulation. Modern applied Bayesian work is integrally tied to Markov chain Monte Carlo simulation (MCMC) tools. We will develop the theory behind MCMC tools and then learn how to use these tools, using WinBUGS (a well-known, high quality, free program).
Workshop Materials
Workshop materials are available only to participants.
To obtain the username and password, please e-mail causalinference@law.northwestern.edu