Advanced 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.