2015 Main Causal Inference Workshop

We would like to invite you to attend the Sixth annual workshop on Research Design for Causal Inference, sponsored by Northwestern University, Duke University, and the Society for Empirical Legal Studies.

Monday-Friday, July 13-17, 2015, at Northwestern Law School, 375 East Chicago Avenue, Chicago, IL

Note: The main workshop is close to capacity for 2015. We have reserved a limited number of spaces for: faculty attendees. Graduate students and post-docs who would like to attend should email Bernie Black to be added to the waiting list. In offering any available spots to persons on the waiting list, we will give priority to attendees who will attend both workshops.

An Advanced Workshop on Research Design for Causal Inference will be held this year on July 19-22, 2015, at Northwestern Law School.

Teaching Faculty and Organizers | Registration | Schedule (Detailed Schedule) | Materials (login required) | Hotels | Wireless Access | Stata 14 Instructions | Stata Tutorial Day 1 | Stata Tutorial Day 2 | Participant List | Breakout Sessions

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 simulations and quasi-experiments, 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 the kinds of causal inferences one can and cannot 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.

Statistical software
We will use Stata, and will provide attendees with a temporary Stata14 license. Versions 10, 11, 12, and 13 should be fine for most purposes (but you may need to "downconvert" datasets. Each day will conclude with a Stata "workshop" where we will illustrate selected methods with real data and Stata code. More advice for Stata novices (pdf).

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. For recent PhD's, there will be overlap with what you already know, but 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. Web page, with link to CV: www.stat.harvard.edu/faculty_page.php?page=rubin.html; Wikipedia: http://en.wikipedia.org/wiki/Donald_Rubin
  • Stephen L. Morgan (Johns Hopkins, Sociology and Education) Stephen Morgan is Bloomberg Distinguished Professor of Sociology and Education, Johns Hopkins University. He is a co-author (with Christopher Winship of Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed. 2015). Principal research interests:  inequality, education, and demography.
  • 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.

Conference Organizers

  • Bernard Black (Northwestern University)
    Nicholas J. Chabraja Professor at Northwestern University School of Law, with a secondary appointment at Kellogg School of Management, Department of Finance. 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.  Papers on SSRN


Registration and Workshop Cost

Click here to register

Registration deadline: July 3, 2015 (but the main workshop is likely to fill early)

Main workshop: Tuition is $850 ($500 for graduate students and post-doctoral fellows; $350 for Northwestern or Duke-affiliated attendees).

Advanced workshop: Tuition is $700 ($400 for graduate students and post-doctoral fellows; $250 for Northwestern or Duke-affiliated attendees). Three-day option: The Sunday session (on simulation and bootstrapping) can be skipped without loss of continuity. The 3-day cost is $560 ($320 for graduate students and post-docs; $200 for Northwestern or Duke affiliates).

Combined workshop discount: 40% discount on the advanced workshop for those who attend both workshops. (Does not apply to Northwestern or Duke affiliates).

Each workshop fee includes all materials, a temporary Stata13 license, breakfast, lunch, snacks, and Monday evening reception. All amounts will increase by $50 in May 13th, but we may fill up before then.

Workshop Schedule

Monday, July 13 (Don Rubin)

Registration and breakfast: 8:00

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.  Choosing estimands (the science), and how the estimand affects research design.  

Tuesday-Wednesday, July 14-15 (Stephen Morgan)

Designs for “Pure” Observational Studies
Selection [only] on observables and common support assumptions.  Subclassification, matching, reweighting, and regression estimators of average treatment effects.  Propensity score methods.  What to match on: an introduction to directed acyclic graphs.

Instrumental variable 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.

Thursday-Friday, July 16-17 (Jens Hainmueller)

Panel Data and Difference-in-Differences
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.

Regression Discontinuity
(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 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.


Please click here to reserve your room at the Marriott Residence Inn, which is roughly four blocks north of the law school. Rooms for the main workshop are $189 per night plus tax. The deadline to reserve your room is May 13, 2015.

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 Michael Cooper (causalinference@law.northwestern.edu) for logistics and registration.