2012 Workshop on Research Design for Causal Inference
August 6-10, 2012, Northwestern Law School, Chicago, Illinois
We invite you to attend the third (annual) workshop on Research Design for Causal Inference, sponsored by Northwestern University, University of Southern California, and the Society for Empirical Legal Studies. We have recruited a world-class faculty for the workshop - see details below.
REGISTRATION: at the url above. Registration is limited to 90 participants. We filled up quickly last year and are already 60% full for this year, so please register early. See below for cost and other details.
OVERVIEW OF THE CAUSAL INFERENCE WORKSHOP: 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 begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods reflect the goal and are often adapted to the needs of a particular study. Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes.
Each day will include a Stata "workshop" where we 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), sociology, education, psychology, etc. - indeed anywhere that causal inference is important.
MINIMUM PRIOR KNOWLEDGE: We will assume knowledge, at the level of an upper-level college econometrics or applied statistics course, of how to run multivariate regressions, including OLS, logit, and probit; familiarity with basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables are and how they are used.
Despite its modest prerequisites, this course should be suitable for most researchers with PhD level training and for empirical legal scholars with reasonable even if more limited training. Especially 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.
Donald B. Rubin (Harvard University, Department of Statistics)
Donald Rubin is John L. Loeb Professor of Statistics, Harvard University. His work on what today is often called 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: http://www.stat.harvard.edu/faculty_page.php?page=rubin.html; Wikipedia: http://en.wikipedia.org/wiki/Donald_Rubin
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: http://www.econ.berkeley.edu/~jmccrary
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 evaluation. Web page with link to CV: http://www.hks.harvard.edu/fs/aabadie. Papers on SSRN: http://ssrn.com/author=198468
Jens Hainmueller (MIT, Political Science)
Jens Hainmueller is Associate Professor at the Massachusetts Institute of Technology. Principal research interests: applied statistics, immigration, political economy, program evaluation. Web page with link to CV and synthetic controls software: http://www.mit.edu/~jhainm
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. Web page with link to CV: http://www.kellogg.northwestern.edu/Faculty/Directory/Black_Bernard.aspx . Papers on SSRN: http://ssrn.com/author=16042
Mathew McCubbins (University of Southern California)
Mat McCubbins is Provost Professor of Business, Law and Political Economy at University of Southern California, with positions in the Marshall School of Business, the Gould School of Law, and the Department of Political Science. Principal research interests: legislative organization; communication, learning and decisionmaking; research design; network economics. Web page with link to CV: http://weblaw.usc.edu/who/faculty/directory/contactInfo.cfm?detailID=1432 . Papers on SSRN: http://ssrn.com/author=17402
REGISTRATION AND WORKSHOP COST: Tuition is $850; with a discounted rate of $500 for graduate students (PhD, SJD, or law) and post-doctoral fellows. The workshop fee includes all materials, a temporary Stata license, breakfast, lunch, snacks, and Monday evening reception. All amounts will increase by $50 on July 2, 2012 (but we're likely to fill up well before then). Registration deadline: July 20, 2012. You can register online at the url above. You can cancel by July 2 for a 75% refund and by July 20 for a 50% refund (in each case, less credit card processing fee), but there are no refunds after that.
For Northwestern or USC-affiliated attendees, we will charge only $300 (basically our marginal cost for meals and incidental expenses) if you in fact attend, but will charge the regular rate if you register and then don't come, because then you occupied a spot we could have provided to someone else.
QUESTIONS ABOUT THE WORKSHOP: Please email Bernie Black (email@example.com) or Mat McCubbins (firstname.lastname@example.org) for substantive questions or fee waiver requests, and Michael Cooper ( email@example.com) for logistics and registration.
Monday August 6 (Don Rubin)
- 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. Multiple imputation of missing potential outcomes.
Tuesday August 7 (Justin McCrary)
- Instrumental variables (IV), including (i) the core (untestable) need to satisfy the "only through" exclusion restriction, (ii) heterogeneous treatment effects; (iii) randomized trials or quasi-experiments with noncompliance
- Reading: Angrist and Pischke, Mostly Harmless Econometrics, chaps. 2, 4
- (Regression) discontinuity (RD) research designs: sharp and fuzzy designs; bandwidth choice; need to test (not just assume) covariate balance; discontinuities as substitutes for true randomization and as sources of convincing instruments
Wednesday, August 8 (Alberto Abadie)
- Selection (only) on observables
- Matching and subclassification
- Comparing matching to regression
- Propensity score methods
Thursday, August 9 (Alberto Abadie)
- Handling poorly matched observations
- Introduction to difference-in-differences
- Standard error issues (robust and clustered standard errors, bootstrapping)
Thursday post-lunch talk (Bernie Black): Bloopers
- Examples, drawn from different areas, of how to get causal inference wrong
Friday morning August 10) (Jens Hainmueller)
- Panel data: Fixed and random effects, and the choice between them
- Synthetic controls and other advanced DiD topics
Friday afternoon: Feedback on your own research
- Attendees will have an opportunity to present their own research questions from current work, and get feedback, in breakout sessions (session leaders: Bernie Black, Mat McCubbins, Jens Hainmueller)