Donald B. Rubin, Harvard University
Techniques for Drawing Causal Inferences from Imperfect Studies
Thursday, May 15 at 7:30pm in Physics-Astronomy A110
This lecture will focus on straightforward techniques for
estimating causal effects in observational studies, primarily
methods of subclassification and matching using propensity scores.
Medical and economic examples will be used to illustrate the
technology and to show that answers from observational studies can
track those from similar randomized experiments when appropriate
covariates are controlled by propensity score methods.
Specifically, the medical example concerns experimental and
observational data on breast cancer treatments, and the economic
example concerns experimental and observational data on a
job-training program; if time permits, a new study on the effects of
income infusion (winning the lottery) will also be discussed.