Deadline: August 13, 2014

Links More Information and Registration

Northwestern University and Duke University are holding two workshops on Research Design for Causal Inference this year.  We invite you to attend either or both.  Apologies for the length of this message, which covers both.

Main workshop:  Monday – Friday, July 7-11, 2014 [at Northwestern]

Advanced workshop:  Wednesday - Friday, August 13-15, 2014 [at Duke]

Both workshops will be taught by world-class causal inference researchers.  See below for details.  Registration for each is limited to 100 participants.  We filled the main workshop quickly last year, so please register soon.

For information and to register:

Bernie Black [Northwestern, Law School and Kellogg School of Management]

Mat McCubbins [Duke, Political Science and Law]

Main 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, and 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 what 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 discuss 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 causal inference and on which methods to use with messy, real-world datasets and limited sample sizes.  Each day will include with a Stata “workshop” to illustrate selected methods with real data and Stata code.

Advanced Workshop Overview:  The advanced workshop seeks to provide an in-depth discussion of selected topics that are beyond what we can cover in the main workshop.  Principaltopics for 2014 include:  Day 1:  Choosing estimands (the science), and how choice of estimand affects research design.  Principal stratification methods (a little known, but very powerful extension of the always taker/never-taker/complier/defier categories developed in “causal IV”); advanced matching methods; multiple imputation of missing potential outcomes.  Day 2:  Simulation studies; bootstrap methods; advanced topics in regression discontinuity design.  Day 3:  Causal inference with panel data. Topics will include  handling treatment heterogeneity, handling time dynamics, synthetic controls, marginal structural models, and standard errors. 

Target audience for Main Workshop:  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. – indeed anywhere that causal inference is important.

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.

Target Audience for Advanced Workshop.  Our target audience is empirical researchers who are reasonably familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge.  We will assume familiarity with the potential outcomes notation, randomization inference, difference-in-differences, regression discontinuity, panel data, and instrumental variable designs, but will not assume expertise in any of these areas.

Main workshop faculty

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.  Web page with link to CV:

Alberto Abadie (Harvard University, Kennedy School of Government)

Alberto Abadie is Professor of Public Policy at the Kennedy School of Government at Harvard University.  Principal research interests: econometrics; program evaluationWeb page with link to CV: .  Papers on SSRN:

Jens Hainmueller (Stanford, Political Science)

Jens Hainmueller is Associate Professor in the Stanford Political Science Department.  He also holds a courtesy appointment in the Stanford Graduate School of Business.  His research interests include statistical methods, political economy, and political behavior.  Web page with link to CV:


Main workshop outline

Monday-Tuesday July 7-8 (Justin McCrary)

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.

Instrumental variable and regression discontinuity methods

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.

(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.

Wednesday July 9; Thursday morning July 10 (Alberto Abadie)

Observational Studies:  Selection on observables

Selection on observables and common support assumptions.  Subclassification, matching, and regression estimators of average treatment effects. Propensity score methods: matching and weighting. What to match on: a brief introduction to directed acyclic graphs.

Standard Errors

Robust and clustered standard errors. The bootstrap.

Thursday afternoon, July 10 – Friday morning, July 11 (Jens Hainmueller)

Difference-in-Differences, Panel Data, and Synthetic Controls

Simple two-period DiD; the “parallel changes” assumption.  Leads and lags and distributed lag models.  Accommodating covariates.  Triple differences.  Panel data methods.  Synthetic controls.

Friday afternoon:  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, Mat McCubbins, Jens Hainmueller.  Parallel sessions as needed to meet demand.



Advanced Workshop Faculty

Donald B. Rubin (Harvard University, Department of Statistics)

Donald 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.  Web page, with link to CV:; Wikipedia:

Jonathan N. Katz (California Institute of Technology)

Jonathan Katz is Kay Sugahara Professor of Social Sciences and Statistics at Caltech.  Co-editor:  Political Analysis.  Principal research interests: American  politics, political methodology; formal political theory.  Web page with link to CV:

Justin McCrary (University of California, Berkeley, Law School) [see blurb for main workshop above]

Advanced Workshop Outline

Wednesday August 13 (Don Rubin)

Choosing estimands (the science).  Implications of choice of estimand for choice of method.  Principal stratification.  Flexible matching methods.  Multiple imputation of missing potential outcomes.  And whatever else Don thinks he should cover, in the allotted time.

Thursday August 14 (Justin McCrary)

Conducting simulation studies.  Inference and testing using the bootstrap, including adapting bootstrap methods to your research design.  Topics in regression discontinuity design:  nonparametric estimation; Local linear regression and density estimation; choosing bandwidth and assessing sensitivity to bandwidth choice.

Friday August 14 (Jonathan Katz)

Topics in causal inference with panel data, including time-series-cross-sectional (TSCS) data. Topics will include  issues of unit heterogeneity, specification of dynamics, synthetic matching, and marginal structural models, and which standard errors to use. 

Lunch talk:  Advice from a journal editor on what to do (and not do) (Jonathan Katz is the editor of Political Methodology).


Registration and Workshop Cost

Main workshop tuition is $850 ($500 for graduate students (PhD, SJD, or law) and post-docs).  Advanced workshop tuition is $550 ($350 for graduate students and post-docs).  There are additional discounts (to $350 and $200) for Northwestern or Duke-affiliated attendees.  The workshop fees include all materials, temporary Stata13 license, breakfast, lunch, snacks, and an evening reception on the first day of each program.  All amounts will increase by $50 roughly two months before the workshop (May 22 for the main workshop, but this workshop is likely to fill up before then).  See website for registration deadlines and cancellation policy.  We know the workshops are not cheap.  We use the funds to pay our speakers and for meals and other expenses; we don’t pay ourselves.

Workshop Organizers

Bernard Black (Northwestern University, Law and Kellogg School of Management)

Bernie Black is Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School and Kellogg School of Management.  Principal research interests: law and finance, international corporate governance, health law and policy; empirical legal studies.  Papers on SSRN:

Mathew McCubbins (Duke University)

Professor of Political Science and Law at Duke University, with positions in the Law School and the Political Science Department, 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.  Web page with link to CV: Papers on SSRN:

Questions about the workshops:  Please email Bernie Black ( or Mat McCubbins ( for substantive questions or fee waiver requests, and Michael Cooper ( for logistics and registration.