Advanced Causal Inference Workshop

Monday – Wednesday, August 15-17, 2022

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.


Northwestern Pritzker School of Law
375 East Chicago Avenue, Chicago, IL

Overview  |  Teaching Faculty and Organizers  |  Registration  |  Schedule  |  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 2022 quantile and nonlinear difference-in-differences, doubly robust estimation of causal effects; DiD methods that address staggered treatments (applied to different units at different times); and the application of machine learning methods to causal inference.

Target Audience for Advanced Workshop

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

We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. 

  • 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.

  • Yiqing Xu (Stanford University)
    Yiqing Xu is Assistant Professor of Political Science at University of California, San Diego. His main methods research involves causal inference with panel data.

  • 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 has chiefly been in the areas of the 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. 

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, international corporate governance.  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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Registration and Workshop Cost

Tuition for the Advanced Workshop is $600 ($400 for post-docs and graduate students; $300 if you are Northwestern affiliated).

Tuition for the Main Workshop is $900 ($600 for post-docs and graduate students PhD, SJD, or law; $500 if you are Northwestern affiliated).

There is a $200 discount for attending both workshops ($100 if Northwestern-affiliated).

Zoom option: We’ve decided to charge 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, depending on how travel and COVID-19 risk looks by mid-summer.

Vaccination Recommended:  Northwestern no longer requires proof of vaccination for on-campus events. However, we still strongly recommend that you be vaccinated against COVID-19 (2-doses) and have received a booster shot if the second dose was more than 6 months before the start of the workshop. If you have been infected with COVID – especially if recently infected with the Omicron variant – this can be considered as the rough equivalent to one vaccine dose.

For special circumstances, please contact Professor Black at

The workshop fees include all materials, breakfast, lunch, snacks, and an evening reception on the first workshop day.

We know the workshop is not cheap. We use the funds to pay our speakers and expenses; we don’t pay ourselves.

Registration is limited to 100.

Register Now

You can cancel by July 8, 2022 for a 75% refund (or carry over your registration to next year for full credit) and by July 22, 2022 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that, because we can't realistically replace you. If the workshop is canceled, we will offer a full refund.

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Workshop Schedule

Plan on full days, roughly 9:00-5:00. Breakfast will be available at 8:30.

Monday, 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. 

Tuesday, August 16

Advanced panel data methods
Yiqing Xu

Causal inference with panel data using parametric, semi-parametric, non-parametric methods for addressing imbalance between treated and control units. Bias in classic DiD models using two-way fixed effects. Topics include interactive fixed effects and matrix completion methods, as well as reweighting approaches such as panel matching, trajectory balancing and augmented synthetic control. Relative strengths and weaknesses of different methods will be discussed.

Wednesday, August 17

Introduction to machine learning (predictive inference)
Christian Hansen

Introduction to “machine-learning” approaches to prediction algorithms.  High-dimensional model selection (function classes, regularization, tuning), model combination (ensemble models, bagging, boosting), model evaluation, and implementation.

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.

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There are many hotels within walking distance to Northwestern Law School, in all price ranges. There are also many air B&B options. If you want to share with another attendee, please send an email to Sebastian Bujak at and we will circulate a list of interested attendees as we get closer to the workshop data. Please be aware that hotel rates will likely increase as we get closer to the conference. 

Millennium Knickerbocker Hotel

  • 163 E Walton Place, 0.5 miles from Northwestern
  • Current Rate as of 4/13/22: $105 

Selina Chicago

  • 100 E Chestnut Ave, 0.5 miles from Northwestern
  • Current Rate as of 4/13/22: $108
  • Please note, they have shared room options. 

Warwick Allerton

  • 701 N Michigan Ave, 0.4 miles from Northwestern
  • Current Rate as of 4/13/22: $166 

Hampton Inn

  • 160 E Huron St , 0.4 miles from Northwestern
  • Current Rate as of 4/13/22: $175

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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