2017 Main Causal Inference Workshop
We would like to invite you to attend the Eighth Annual Workshop on Research Design for Causal Inference, sponsored by Northwestern University and Duke University.
Monday-Friday, June 19-23, 2017, at Northwestern Pritzker School of Law, 375 East Chicago Avenue, Chicago, IL
Teaching Faculty and Organizers | Registration | Schedule | Detailed Schedule | Hotels | Materials (login required)
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 causal inference and on 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.
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.
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
- Alberto Abadie (Massachusetts Institute of Technology, Department of Economics)
Alberto Abadie is Professor of Economics at the Massachusetts Institute of Technology. Principal research interests: econometrics; program evaluation. Principal research interests: econometrics; program evaluation. Papers on SSRN
- 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.
- Bernard Black (Northwestern University)
Nicholas J. Chabraja Professor at Northwestern University, with positions in the Law School, Institute for Policy Research, 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. Papers on SSRN
Please click here to register.
Main workshop: Tuition is $850 ($500 for graduate students (PhD, SJD, or law) and post-docs; $350 for Northwestern or Duke-affiliated attendees).
The workshop fees include all materials, temporary Stata 14 license, breakfast, lunch, snacks, and an evening reception on the first workshop day.
Amounts will increase by $50 on May 1, 2016 (but the workshop is likely to fill up before then).
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 16, 2017 for a 75% refund and by June 1, 2017 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that.
Monday, June 19 (Don Rubin): 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. Rerandomization. One-sided and two-sided noncompliance.
Tuesday, June 20 (Alberto Abadie): Designs for “Pure” Observational Studies
The core, untestable requirement of selection [only] on observables. Ensuring covariate balance and common support. Subclassification, matching, reweighting, and regression estimators of average treatment effects. Propensity score methods.
Wednesday, June 21 (Alberto Abadie): 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, June 22 (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.
Friday morning, June 23 (Jens Hainmueller): 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. Additional parallel sessions if needed to meet demand.
Stata and R sessions
On Tuesday and Wednesday we will either run parallel Stata and R sessions to illustrate actual code to implement the designs discussed in the lectures. For Thursday and Friday, Jens Hainmueller will build Stata code into his lecture slides.
Please click here to reserve your hotel room.
Questions about the workshops: Please email Bernie Black (firstname.lastname@example.org) or Mat McCubbins (email@example.com) for substantive questions or fee waiver requests, and Michael Cooper (firstname.lastname@example.org) for logistics and registration.