Main Causal Inference Workshop
Monday-Friday, August 3-7, 2026
9 am - about 5 pm
We are excited to be holding our 15th 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 Monday, August 10 through Wednesday, August 12. An optional machine learning primer will be held on Sunday afternoon, August 9.
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 (including faculty, graduate students, post-docs, and other researchers) 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, Statistics)
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
- Scott Cunningham (Baylor University, Economics)
Scott Cunningham is Ben H. Williams Professor of Economics at Baylor University, and the author of Causal Inference: The Remix (2d ed. forthcoming 2026). Principal research interests: mental healthcare; suicide; corrections; sex work; abortion policy; drug policy. - Yiqing Xu (Stanford University, Political Science)
Yiqing Xu is Assistant Professor of Political Science at Stanford and a Faculty Affiliate at the Stanford Center for Causal. His research focuses on political methodology (particularly methods for causal inference for panel data) and comparative politics (with a focus on China). - Eric Chyn (University of Texas at Austin, Economics)
Eric Chyn is Associate Professor of Economics at University of Texas at Austin. He is an applied microeconomist with interests in labor and public economics, working on understanding the effects of government programs on the long-run outcomes of children. - Heather Royer (University of California, Santa Barbara, Economics)
Heather Royer is Professor of Economics at University of California - Santa Barbara. Her research interests include health, labor, children, and families.
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 - Joshua Lerner (University of Chicago)
Joshua Lerner is a Senior Research Methodologist at NORC at the University of Chicago. He is interested in causal inference, research design, econometrics, and Bayesian statistics, including the intersection of AI with survey methodology, American politics, political ideology, and institutional economics.
Workshop Schedule
Plan on full days, roughly 9:00-4:30. Breakfast will be available at 8:30.
An informal wine-and cheese reception for all attendees will follow on Monday, August 3.
Monday, August 3
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.
Tuesday, August 4
Matching and Reweighting Designs for "Pure" Observational Studies
Scott Cunningham
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. Doubly-robust estimation.
Wednesday, August 5
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. Robust and clustered standard errors. Robust and clustered standard errors. Many faces of DiD. Triple differences.
Thursday, August 6: Morning
Causal instrumental variable methods
Eric Chyn
Reasons for using instrumental variables (IV); causal inference with 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. Connections between IV and fuzzy RD designs.
Thursday, August 6: Afternoon
Feedback on your own research
Session leaders: Bernie Black, Joshua Lerner, Eric Chyn
Attendees will present their own research design questions from current work in breakout sessions and receive feedback on research design. Additional sessions if needed to meet demand.
Friday, August 7: Morning
Regression Discontinuity
Heather Royer
Regression discontinuity (RD) designs: sharp and fuzzy designs; continuity-based methods and bandwidth selection; local randomization methods and window selection; extensions and generalizations of canonical RD setup: discrete running variable, multi-cutoff, multi-score, and geographic designs. RD software website
Friday, August 7: Afternoon
Feedback on your own research
Continuation of the Thursday afternoon feedback sessions.
Stata and R coding
Presenter: Joshua Lerner
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.
We will also provide a repository 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 $950 ($650 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 $200 discount for persons attending both workshops, for combined cost of $1,400 ($900 for post-docs and graduate students; $600 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 – June 23, 2026, for the Main Workshop and June 30, 2026, 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 before each workshop. After these dates no refund will be given, but you can carry over the registration fee to a future workshop.
Hotels
Please ask for Northwestern rates when you book your hotel:
Many attendees find an Airbnb or equivalent to be a good option.
Questions about the Workshop
Please email Bernie Black for substantive questions or fee waiver requests, and Sebastian Bujak for logistics and registration.