[Eeglablist] 2026 Northwestern Main and Advanced Causal Inference Workshops

[Scott Makeig]

*2026 Northwestern Main and Advanced Causal Inference Workshops*

*[please recirculate to others who might be interested]*

We are excited to be holding our 15th 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:

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://urldefense.com/v3/__https://www.law.northwestern.edu/research-faculty/events/conferences/causalinference/__;!!Mih3wA!BHo58dKYEG3StcfCd6U4aM9E1cr-gQx8EJnl6PBFVXflMCSdslWExYqnB-1JMPTrQWQJbCWUBsbIz5d6LJ7XP1Q$ 
<https://urldefense.com/v3/__https://www.law.northwestern.edu/research-faculty/events/conferences/causalinference/__;!!Mih3wA!B129KtgTne7U3rVk6V4XR9LMpv_FtNteB6SCTo0VaIJKpnhw523yUIjatzgjZI5IVgiKdt3dckYxBIJptGTYxOxa-dnyC_FYagk8Qw$>*



*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
<https://urldefense.com/v3/__https:/statistics.fas.harvard.edu/people/donald-b-rubin__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXWnUTGdE$>**
(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
<https://urldefense.com/v3/__https:/www.scunning.com/__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXZsZWyw8$>**
(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
<https://urldefense.com/v3/__https:/yiqingxu.org/__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXX0qcnm0$>**
(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
<https://urldefense.com/v3/__https:/www.ericchyn.com/__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXcpS1FeK$>**
(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
<https://urldefense.com/v3/__https:/sites.google.com/site/heathernroyer/__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXaPCnrfe$>**
(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
<https://urldefense.com/v3/__https:/www.chicagobooth.edu/faculty/directory/h/christian-b-hansen__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXe7lirbG$>**
(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
<https://urldefense.com/v3/__https:/www.chicagobooth.edu/faculty/directory/h/christian-b-hansen__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXe7lirbG$>**
(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
<https://urldefense.com/v3/__https:/www.goodman-bacon.com/__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXVmDrKcG$>**
(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:  **T
<https://urldefense.com/v3/__https:/tslocz.github.io/__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXaXz4Lo-$>ymon
Słoczyński
<https://urldefense.com/v3/__https:/tslocz.github.io/__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXaXz4Lo-$>**
(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
<https://urldefense.com/v3/__http://www.law.northwestern.edu/faculty/profiles/BernardBlack/__;!!Mih3wA!B129KtgTne7U3rVk6V4XR9LMpv_FtNteB6SCTo0VaIJKpnhw523yUIjatzgjZI5IVgiKdt3dckYxBIJptGTYxOxa-dnyC_HEc9A-Fw$>*
(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
<https://urldefense.com/v3/__https:/www.norc.org/about/experts/joshua-lerner.html__;!!Dq0X2DkFhyF93HkjWTBQKhk!VnNmRFUkdPlm5_MRrTNoIZAOImChjK_or2XjXtfgobVUChOQzJype4ZJ_Bv5pxexNxFBIm7u6-g8f4cNWWpUfgnSXPPdXbqrMzoX$>*
(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 at northwestern.edu
<bblack at northwestern.edu>*) for substantive questions or fee waiver
requests, and Sebastian Bujak (*sebastian.bujak at law.northwestern.edu
<sebastian.bujak at law.northwestern.edu>)* for logistics and registration
questions.