If your institution has researchers interested in additional training on research design, information on workshops offered by Northwestern Law School may be of interest.
Research Design for Causal Inference: Summer Workshops at Northwestern
MAIN WORKSHOP (MONDAY – FRIDAY, AUGUST 15-19, 2011): FULL-WEEK INTRODUCTION TO RESEARCH DESIGN FOR CAUSAL INFERENCE: Overview of the core methods for credible causal inference from observational data, 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. Registration deadline: August 2, 2011.
BAYESIAN WORKSHOP (MONDAY – WEDNESDAY, JULY 11-13, 2011): Bayesian methods for causal inference, including multiple imputation of missing “potential outcomes”, Markov chain Monte Carlo (MCMC) simulations, including Gibbs sampling, and other flexible model specifications. Registration deadline: June 24, 2011.
For a day-by-day outline of topics covered, please use the links above.
OVERVIEW OF THE MAIN 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 observational studies. 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. We start 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 use for messy, real-world datasets with limited sample sizes. We will illustrate selected methods with real data and Stata code.
OVERVIEW OF THE BAYESIAN WORKSHOP: 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 the core Bayesian methods for research design and analysis of observational studies.
We begin 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.
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.
TEACHING FACULTY: We are fortunate to have recruited outstanding experts in causal research design to teach the workshop sessions.
PRINCIPAL FACULTY FOR MAIN WORKSHOP:
Joshua Angrist (MIT) (days 1 & 2)
Guido Imbens (Harvard University) (day 3)
Alberto Abadie (Harvard University) (day 4)
Theodore Eisenberg (Cornell University)
Daniel E. Ho (Stanford University) (day 5)
BAYESIAN WORKSHOP FACULTY:
Donald B. Rubin (Harvard University) (day 1)
Jeff Gill (Washington University in St. Louis) (days 2 & 3)
Bernard Black (Northwestern University, Law and Kellogg School of Management)
Mathew McCubbins (University of Southern California)