Advanced Causal Inference Workshop
Optional Machine Learning Primer: Sunday afternoon, August 3, 2025 (1:00 pm - 5:00 pm)
Monday-Wednesday, August 4-6, 2025 (9:00 am - about 5 pm)
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 2025 are application of machine learning methods to causal inference; difference-in-differences methods for staggered treatments (applied to different units at different times); and advanced instrumental variable methods.
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. - Andrew Goodman-Bacon (Federal Reserve Board, Minneapolis)
Andrew Goodman-Bacon is Senior Economist at the Opportunity and Inclusive Growth Institute at the Federal Reserve Bank of Minneapolis. His research interests include economic history, health economics, public economics, and applied econometrics. - Peter Hull (Brown University)
Peter Hull is Professor of Economics at Brown University. His research interests include applied econometrics, crime, discrimination, education, and healthcare.
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 4.
Sunday afternoon, August 3 (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.
Readings
Susan Athey and Guido Imbens (2019), Machine Learning Methods that Economists Should Know About, Annual Review of Economics, 11, 685-725.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2021), An Introduction to Statistical Learning with Applications in R, Second Edition. (Free download at https://www.statlearning.com/), Chapters 2.1, 2.2, 5.1, 6.1, 6.2, 8.1, 8.2, 10.1, 10.2 (and the rest of the book is great)
Mullainathan, Sendhil, and Jann Spiess (2017), Machine Learning: An Applied Econometric Approach, 31(Spring) Journal of Economic Perspectives 87-106.
Monday, August 4
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.
Readings
[also for the introduction on Sunday]: Susan Athey and Guido Imbens (2019), Machine Learning Methods that Economists Should Know About, Annual Review of Economics, 11, 685-725.
Alexandre Belloni, Victor Chernozhukov, Iván Fernández-Val, and Christian Hansen (2017), Program evaluation and causal inference with high-dimensional data, Econometrica, 85(1), 233–298.
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins (2018), Double/debiased machine learning for treatment and structural parameters, Econometrics Journal, 21(1), C1–C68, https://doi.org/10.1111/ectj.12097
Jens Ludwig and Sendhil Mullainathan (2022), Algorithmic behavioral science: Machine learning as a tool for scientific discovery, URL http://dx.doi.org/10.2139/ssrn.4164272, Chicago Booth Research Paper No. 22-15.
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan (2018), Human decisions and machine predictions, The Quarterly Journal of Economics, 133(1), 237–293.
Mullainathan, Sendhil, and Jann Spiess (2017), Machine Learning: An Applied Econometric Approach, 31(Spring) Journal of Economic Perspectives 87-106.
Tuesday, August 5
Advanced Difference-in Differences
Andrew Goodman-Bacon
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.
Main Readings
Baker, Andrew, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, Pedro Sant'Anna (2025), “Difference-in-differences designs: a practitioner's guide.” arXiv preprint: arxiv.org/abs/2503.13323
Roth, Jonathan, Pedro H. C. Sant'Anna, Alyssa Bilinski, John Poe (2023), "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature." Journal of Econometrics 235(2): 2218-2244.
https://www.sciencedirect.com/science/article/pii/S0304407623001318
Additional Useful Readings
Callaway, Brantly, Andrew Goodman-Bacon, and Pedro Sant’Anna (2024), “Difference-in-differences with a continuous treatment.” arXiv preprint: arXiv:2107.02637
Ghanem, Dalia, Pedro Sant'Anna, Kaspar Wuthrich (2025), "Selection and parallel trends." arXiv preprint: arxiv.org/abs/2203.09001
Wednesday, August 6
Advanced Instrumental Variables
Peter Hull
Design vs. model-based identification, weak and many instrument bias, estimating complier characteristics, judge IV, shift-share IV and other "formula" instruments.
Core Readings
Angrist, Joshua, Peter Hull, and Christopher. Walters (2023): Methods for Measuring School Effectiveness, Handbook of the Economics of Education 7: 1-60.
Borusyak, Kirill, Peter Hull, and Xavier Jaravel (2024)): “Design-Based Identification with Formula Instruments: A Review,” Econometrics Journal, forthcoming
Additional Readings
Angrist, Joshua, and Jorn-Steffen Pischke, Mostly Harmless Econometrics, 2008 [Chapter 4.1]
Angrist, Joshua, and Michal Kolesar (2024): One instrument to rule them all: The bias and coverage of just-ID IV, Journal of Econometrics, 240(2): 15398.
Angrist, Joshua and Peter Hull (2023): Instrumental variables methods reconcile intention-to-screen effects across pragmatic cancer screening trials, PNAS 120(51)
Agan, Amanda, Jennifer L. Doleac, and Anna Harvey (2023): Misdemeanor Prosecution, Quarterly Journal of Economics 138(3): 1453-1505.
Borusyak, Kirill, Peter Hull, and Xavier Jaravel (2022): Quasi-Experimental Shift-Share Research Designs, Review of Economic Studies 89
Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift (2020): Bartik Instruments: What, When, Why, and How, American Economic Review, 110(8): 2586-2624.
Borusyak, Kirill ,and Peter Hull (2023): Non-Random Exposure to Exogenous Shocks, Econometrica, 91(6): 2155-2185
Borusyak, Kirill, Peter Hull, and Xavier Jaravel (2024): Negative Weights are No Concern in Design-Based Specifications, AEA Papers and Proceedings, 114: 597-600
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 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 $600 ($400 for post-docs and graduate students; $300 if you are Northwestern affiliated).
Discount for attending both workshops: There is a $200 discount for non-Northwestern persons attending both workshops, for combined cost of $1,300 ($800 for post-docs and graduate students; $650 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, 2025, for the Main Workshop and June 30, 2025, 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 there are no refunds, 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 or Scott Cunningham for substantive questions or fee waiver requests, and Sebastian Bujak for logistics and registration.