2026 Northwestern Main and Advanced Causal Inference Workshops

[please recirculate to others who might be interested]

We are excited to be holding our 15^th annual workshop on Research Design for Causal Inference at Northwestern Law School in Chicago, IL.  We invite you to attend. 

Main Workshop:  Monday – Friday, August 3 - August 7, 2026

Advanced Workshop:  Monday – Wednesday, August 10-12, 2026

Optional Machine Learning Primer:  Sunday afternoon, Aug. 9, 2026

What is special about these workshops:a

1.      World-class speakers working at the frontier of causal inference research

2.      Stata and R Coding sessions with exclusive access to the dedicated repository

3.      Breakout sessions for feedback on your own research

In person-registration is limited to 125 participants for each workshop, so hurry up and register for in person attendance! 

There will also be a Zoom option, but please come in person if you can.  We do our best, but the online experience is not the same.

Get more information and register now: https://www.law.northwestern.edu/research-faculty/events/conferences/causalinference/

 

Detailed information on the workshops

Workshop Overview:  We will cover true randomized experiments and contrast them to natural or quasi experiments and 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 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. The methods are often adapted to a particular study. 

Advanced Workshop Overview:  The advanced workshop provides in-depth discussion of selected topics, beyond what the main workshop covers.  The principal topics for 2026 are application of machine learning methods to causal inference; advanced difference-in-differences methods, and advanced instrumental variable methods.

Target Audience for Main Workshop:  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, 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 and logit; basic probability and statistics; and basic understanding of instrumental variables.  This course should be suitable both for empirical researchers with PhD-level training and for those with reasonable but more limited training.

Target Audience for Advanced Workshop: Empirical researchers who are familiar with the basics of causal inference (from the main workshop or otherwise), and want to extend their knowledge.  We will assume familiarity with potential outcomes, difference-in-differences, and instrumental variable methods.

Main Workshop Outline

Monday, August 3:  Donald Rubin (Harvard University, Statistics)

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.  Experimental design and applications to observational studies.  One-sided and two-sided noncompliance. 

Tuesday, August 4:  Scott Cunningham (Baylor University, Economics)

Matching and Reweighting Designs for “Pure” Observational Studies

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:  Yiqing Xu (Stanford University, Political Science)

Panel Data and Difference-in-Differences

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. Many faces of DiD.  Triple differences. 

Thursday Morning, August 6:  Eric Chyn (University of Texas at Austin, Economics)

Causal instrumental variable methods

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 Afternoon, August 6:  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, Joshua Lerner, Eric Chyn).  Additional sessions if needed to meet demand.

Friday Morning, August 7:  Heather Royer (Univ California, Santa Barbara, Economics)

Regression Discontinuity

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.

Friday Afternoon, August 6 Afternoon:  Feedback on your own research

Continuation of the Thursday afternoon feedback sessions.

Advanced Workshop Outline

Sunday afternoon, August 9 (optional): Christian Hansen (University of Chicago, Booth School of Business)

Primer on machine learning approaches to prediction 

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 10:  Christian Hansen (University of Chicago, Booth School of Business)

Applications of machine learning to causal inference

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 11:  Andrew Goodman-Bacon (Federal Reserve Board, Minneapolis)

Advanced Difference-in Differences

New developments in causal inference in difference-in-differences designs.  Limitations of two-way fixed effects regressions.  Comparison of alternative estimation strategies that have been proposed to address these weaknesses and to accommodate complex treatment variables.  Ways to weaken the parallel trends assumption and to diagnose and/or deal with violations of parallel trends.

Wednesday, August 12:  Tymon Słoczy？ski (Brandeis University, Economics)

Advanced Instrumental Variables

Design vs. model-based identification, weak and many instrument bias, estimating complier characteristics, judge IV, shift-share IV and other "formula" instruments.

Informal Receptions

We plan informal, wine-and cheese receptions for all attendees on Monday August 3 and Monday August 10, following that day’s 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 for Northwestern affiliates.

Advanced Workshop:  tuition is $650 ($450 for post-docs and graduate students; $300 for Northwestern affiliates. 

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, to encourage in-person attendance. 

You can cancel either workshop five weeks in advance, for a 75% refund – by 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 three weeks before each workshop. After these dates no refund, but you can carry over the registration fee to a future workshop.

We know the workshop is not cheap.  We use the funds to pay our speakers and expenses.  Prof. Black does not pay himself.

Workshop Schedule

You should plan on full days, roughly 9:00-4:30 or 5:00.  Breakfast will be available at 8:30.

Workshop Organizers

Bernard Black (Northwestern University)

Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Pritzker School of Law, the Kellogg School of Management, Finance Department, and the Buehler Center for Health Policy and Economics in the Feinberg School of Medicine.  Principal research interests: health law and policy; empirical legal studies, law and finance.

Joshua Lerner (NORC at the University of Chicago)

Joshua Lerner is 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. 

Stata and R coding

On selected days, we will run combined Stata and R sessions, to illustrate code for the research designs discussed in the lectures.  Some speakers will also build Stata or R code into their lecture slides.  Presenter:  Joshua Lerner.  We will also provide a repository of datasets and code (in Stata, R, and Python) to illustrate the methods presented in the workshop.

Questions about the workshops:  Please email Bernie Black (bblack@northwestern.edu) for substantive questions or fee waiver requests, and Sebastian Bujak (sebastian.bujak@law.northwestern.edu) for logistics and registration questions.