Donald B. Rubin, Harvard University
Drawing Causal Inferences from Imperfect Studies with
Nonignorable Treatment Assignment
Friday, May 16 at 12:30pm in Smith 120
This second lecture will focus on more sophisticated methods
applicable when too few covariates are available to make it
plausible that treatment assignment is ignorable (i.e., conditionally
randomized given the covariates). The template setting involves
randomized experiments with noncompliance where
"use-effectiveness" (i.e., the effect of exposure to the treatment,
not the effect of assignment to the treatment) is the estimand. The
techniques that will be presented combine extensions of
instrumental variables ideas from economics and Bayesian posterior
analysis implemented by MCMC methods. Despite their relative
sophistication, the methods and resultant analyses are easily
understood, and will be illustrated using an experiment in Indonesia
on the effects of Vitamin A on mortality and an experiment in
Indiana on the effects of flu shots on flu-related hospitalization.