Causal Inference: Bayesian Methods Workshop

We would like to invite you to attend the workshop on Bayesian Methods for Causal Inference, sponsored by Northwestern University, University of Southern California, the Society for Empirical Legal Studies, and the Searle Center on Law, Regulation, and Economic Growth. We have recruited a world-class faculty for the Bayesian workshop.

Dates and Location: July 11-13, 2011, at Northwestern Law School, Chicago, IL

We will hold our main 5-day Workshop on Research Design for Causal Inference, which emphasizes frequentist methods, in August 2011.

Teaching Faculty and Organizers | Registration | Workshop Schedule| Workshop Materials

Overview of the Bayesian Causal Inference Workshop
Research design for causal inference is at the heart of a "credibility revolution" in empirical research. Credible causal inference often requires researchers not to rely on the linearity and normality assumptions underlying classical regression. Bayesian imputation and simulation methods provide many of the analytic tools for doing so. We will cover Bayesian methods for research design and analysis for true randomized experiments, as well as for quasi-experiments where part of the sample is "treated" in some way, the control group is drawn from the rest of the sample, but the researcher controls neither the assignment of units to treatment nor administration of the treatment. These analytic methods include multiple imputation of missing "potential outcomes", using Markov chain Monte Carlo (MCMC) simulations, including Gibbs sample, and other flexible model specifications.

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 the centrality of research design, and discuss how Bayesian methods allow research designs that better achieve that goal.

The workshop will include an introduction to Winbugs, the principal public domain Bayesian inference software. We will illustrate selected methods with real data and Winbugs 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) 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. Wikipedia
  • Jeff Gill (Washington University in St. Louis) is Director of the Center for Applied Statistics, Professor of Political Science and Professor of Mathematics at Washington University in St. Louis, and the author of Bayesian Methods: A Social and Behavioral Approach (second edition 2007). Principal research interests: political methodology, Bayesian research methods; statistical computing. Wikipedia

Conference Organizers

  • Bernard Black (Northwestern University, Law and Kellogg School of Management) 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 (University of Southern California) 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. Papers on SSRN

Registration and Workshop Cost
Registration is limited to 60 participants.

Tuition is $500 if you register by June 3; with a discounted rate of $300 for graduate students (PhD, SJD, or law) and post-doctoral fellows. The workshop fee includes all materials, a Winbugs license (which is free anyway), breakfast, lunch, and snacks. Registration deadline: June 24, 2011. You can register online at the url above. These amounts increase by $50 after June 3. You can cancel by June 3 for a full refund and by June 24 for a 75% 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 $125 (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).

Please email Bernie Black or Mat McCubbins for substantive questions or fee waiver requests, and Timothy Jacobs or for logistics and registration.

Workshop Schedule

The workshop will run for three full days from 9:00 a.m.-4:30 p.m., plus a reception on Monday from 4:30 p.m.-6:00 p.m. There will be roughly six 50-min lectures each day, plus a lunch break from roughly 12:15 p.m.-1:30 p.m. The starting time is fixed, all else is approximate.

  • Monday July 11 (Don Rubin)
    The "potential outcomes (with assignment mechanism) or "Rubin causal model" approach to causal inference. Understanding the assignment mechanism. Estimating treatment effects using multiple imputation of the missing outcomes for both treated and control units. Comparison of multiple imputation to regression methods. Research design for credible causal inference. Applications to randomized experiments and to "natural" or quasi-experiments with "unconfounded" assignment of units to treatment.
  • Tuesday July 12 (Jeff Gill)
    The basics of Bayesian inference, including the probabilistic treatment of uncertainty, prior and posterior distributions, updating knowledge, and describing results. Comparison of Bayesian to frequentist approaches. Bayesian approaches to construction and assessment of linear and nonlinear regression-style models.
  • Wednesday, July 13 (Jeff Gill)
    Bayesian stochastic simulation. Modern applied Bayesian work is integrally tied to Markov chain Monte Carlo simulation (MCMC) tools. We will develop the theory behind MCMC tools and then learn how to use these tools, using WinBUGS (a well-known, high quality, free program).

Workshop Materials

Workshop materials are available only to participants.


To obtain the username and password, please e-mail

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