2018 Main Causal Inference Workshop

We would like to invite you to attend the Ninth Annual Workshop on Research Design for Causal Inference, sponsored by Northwestern University and Duke University.

Monday-Friday, June 18-22, 2018, at Northwestern Pritzker School of Law, 375 East Chicago Avenue, Chicago, IL

Our "Advanced" Workshop on Research Design for Causal Inference will be held this year on June 25-27, 2018, at Northwestern Pritzker School of Law.

Teaching Faculty and Organizers | Registration | Schedule | Hotels 

Workshop Overview

Research design for causal inference is at the heart of a “credibility revolution” in empirical research. 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 in some way, the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the causal inferences one can draw from a research design, threats to valid 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 emphasize how to design research to come closer to that goal. The methods are often adapted to a particular study. Some of the methods are covered in PhD programs, but rarely in depth, and rarely with a focus on credible causal inference and which methods to use with messy, real-world datasets and limited sample sizes. Several workshop days will include a Stata “workshop” to illustrate selected methods with real data and Stata code.

Target audience
Quantitative empirical researchers (faculty and graduate students) 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.

Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or similar course, of multivariate regression, including OLS, logit, and probit; basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables.  Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable but more limited training.  Even for recent PhD’s, there will be much that you don’t know, or don’t know as well as you should.

Teaching Faculty

We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions. The faculty are listed in order of appearance.

  • Donald Rubin (Harvard University, Department of Statistics)
    Rubin is John L. Loeb Professor of Statistics, Harvard University. His work on the "Rubin Causal Model" is central to modern understanding of when one can and cannot infer causation from regression. Principal research interests: statistical methods for causal inference; Bayesian statistics; analysis of incomplete data. Wikipedia page
  • Justin McCrary (University of California, Berkeley, Law School)
    Justin McCrary is Professor of Law, University of California, Berkeley.  Principal research interests: crime and urban problems, law and economics, corporations, employment discrimination, and empirical legal studies.  
  • Jens Hainmueller (Stanford University, Department of Political Science)
    Jens Hainmueller is Professor in the Stanford Political Science Department, and co-Director of the Stanford Immigration Policy Lab.  He also holds a courtesy appointment in the Stanford Graduate School of Business.  His research interests include statistical methods, political economy, and political behavior. Papers on SSRN

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
  • Mathew McCubbins (Duke University)
    Professor of Political Science and Law at Duke University, with positions in the Political Science Department and the Law School, and director of the Center for Law and Democracy.  Principal research interests: democratic institutions, legislative organization; behavioral experiments, communication, learning and decisionmaking; statutory interpretation, administrative procedure, research design; network economics. Papers on SSRN

Registration and Workshop Cost

Online registration is now closed. Please email laura.dimitrijevic@law.northwestern.edu if you are interested in registering.

Main workshop: Tuition is $900 ($600 for graduate students (PhD, SJD, or law) and post-docs). 

The workshop fees include all materials, temporary Stata 15 license, 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 for meals and other expenses; we don’t pay ourselves.

You can cancel by May 14, 2018 for a 75% refund and by May 28, 2018 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that.

Workshop Schedule

Monday, June 18 (Donald Rubin):

Introduction to Modern Methods for Causal Inference (9:30am-12:30pm, 1:30pm-3:30pm)
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. Choosing estimands (the science), and how the estimand affects research design. One-sided and two-sided noncompliance.

Monday Reception (4:00-5:30pm)

Tuesday, June 19 (Justin McCrary):

Designs for “Pure” Observational Studies (9:00am-12:00pm, 1:45-3:45pm)
The core, untestable requirement of selection [only] on observables. Common support assumptions. Subclassification, matching, reweighting, and regression estimators of average treatment effects. Propensity score methods.

Tuesday Lunch Talk (Don Rubin, 12:30-1:30pm)
Some statistical bloopers

Stata-based examples (Bernie Black, 3:45-4:45pm)
SIntended as a gentle introduction, for people who know a little bit about Stata, to how to use Stata to implement some of the methods we will discuss during the week. If you are a novice Stata user, there are introductory materials on the course folder at Northwestern Box\Causal Inference Workshops\Stata and R materials.

R-based examples (Josh Lerner, 3:45-4:45pm)
Similar, but using R rather than Stata.

Wednesday, June 20 (Justin McCrary):

Instrumental variable methods (9:00am-12:00pm, 1:15-3:45pm)
Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.

Stata- and R-based examples (Bernie Black and Josh Lerner, 3:45-4:45pm)
Causal inference with instrumental variables (IV), including (i) the core, untestable need to satisfy the “only through” exclusion restriction; (ii) heterogeneous treatment effects; and (iii) intent-to-treat designs for randomized trials (or quasi-experiments) with noncompliance.

Thursday, June 21 (Jens Hainmueller):

Panel Data and Difference-in-Differences (9:00am-12:00pm; 1:45-4:45pm)
Panel data methods: pooled OLS, random effects, correlated random effects, and fixed effects. Simple two-period DiD. The core “parallel changes” assumption. Testing this assumption. Leads and lags and distributed lag models. When does a design with unit fixed effects become DiD? Accommodating covariates. Triple differences. Robust and clustered standard errors.

Thursday lunch talk: Bloopers in Research Design: How Smart People Get Causal Inference Wrong (Bernie Black, 12:30-1:30pm)
Examples, drawn from different areas, of how to get causal inference wrong. 

Friday, June 22 (Jens Hainmueller):

Regression Discontinuity (9:00am-12:00pm)
(Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; testing for covariate balance and manipulation of the threshold; discontinuities as substitutes for true randomization and sources of convincing instruments.

Friday lunch talk: Bloopers with Data: How to Really Examine Your Data (with Examples) (Bernie Black, 12:30-1:30pm)

Friday afternoon: Feedback on your own research (1:45-5:15pm)
Attendees will have an opportunity to present their own research design questions from current work in breakout sessions. Goal: obtain feedback on research design; not present results from a complete paper. [We ask presenters to stay for the full session, and can’t promise an early slot for those who must leave early.] (15 min to present, 15 min discussion). Session leaders: Bernie Black, Jens Hainmueller, Mat McCubbins; we’ll add additional sections as needed.


Hotels accommodations are available at the Residence Inn Chicago Downtown/Magnificent Mile at a rate of $224 per night. Reservations must be made by May 27, 2018 to receive this special rate. Click here to reserve your hotel room or call the hotel directly at 800-331-3131 and reference the "Northwestern Causal Inference Main Workshop" to book your room. 

If you are interested in a room share, email causalinference@law.northwestern.edu. A list of those interested will be shared at a later date. 

Questions about the workshops

Please email Bernie Black (bblack@northwestern.edu) or Mat McCubbins (mathew.mccubbins@duke.edu) for substantive questions or fee waiver requests, and Laura Dimitrijevic (causalinference@law.northwestern.edu) for logistics and registration.

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