13 October 2009
We hope you can join us at the Applied Statistics workshop this Wednesday, October 14th at 12 noon, when we will be happy to have Weihua An, a graduate student in the Sociology Department here at Harvard. Weihua will be presenting "Bayesian Propensity Score Estimators: Simulations and Applications." He has provided the following abstract:
Despite their popularity, conventional propensity score estimators (PSEs) do not take into account the estimation uncertainties in the propensity score into causal inference. This paper develops Bayesian propensity score estimators (BPSEs) to model the joint likelihood of both the outcome and the propensity score in one step, which naturally incorporate such uncertainties into causal inference. Simulations show that PSEs treating estimated propensity scores as if they were known will overestimate the variation in treatment e_ects and result in overly conservative inference, whereas BPSEs will provide corrected variance estimation and valid inference. Compared to other direct adjustment methods (E.g., Abadie and Imbens 2009), BPSEs are guaranteed to provide positive variance estimation, more reliable in small samples, and more flexible to contain complex propensity score models. To illustrate the proposed methods, BPSEs are applied to evaluating a job training program.