Stat Cafe - Gözde Sert
Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects
- Time: Wednesday 1/28/2026 from 12:30PM to 1:30 PM
- Location: BLOC 457
Description
We propose a semiparametric Bayesian methodology for estimating the average treatment effect (ATE) within the potential outcomes framework using observational data with high-dimensional nuisance parameters. Our approach introduces a Bayesian debiasing procedure that corrects for bias arising from nuisance estimation and employs a targeted modeling strategy based on summary statistics rather than the full data. These summary statistics are identified in a debiased manner, enabling estimation of nuisance bias via weighted observables and facilitating hierarchical learning of the ATE. By combining debiasing with sample splitting, the proposed method separates nuisance estimation from inference on the target parameter, thereby reducing sensitivity to nuisance model misspecification. We establish that the marginal posterior for the ATE satisfies a Bernstein–von Mises theorem when both nuisance models are correctly specified, and remains consistent and robust when only one is correctly specified, achieving Bayesian double robustness. Extensive simulations confirm the theoretical results, demonstrating accurate point estimation and credible intervals with nominal coverage, even in high-dimensional settings.
Our Speaker
Gözde Sert is a Ph.D. candidate in Statistics at Texas A&M University, advised by Prof. Anirban Bhattacharya and Prof. Abhishek Chakrabortty. She holds a Master’s degree in Mathematics from The Pennsylvania State University. Her research focuses on Bayesian semiparametric inference, semi-supervised learning, causal inference, missing data and debiasing methods for modern high-dimensional data problems. She is the recipient of multiple competitive research and travel awards, including the ASA Section on Bayesian Statistical Science Student Paper Award and several international conference travel awards.