Main Causal Inference Workshop

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.


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.  

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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. 


  • 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.


  • 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. 


  • 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


See, 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. 


  • 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. 

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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. 

Register Now

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. 

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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.


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