Title: A robust and efficient approach to treatment effect estimation based on sparse sufficient dimension reduction
Speaker: Shujie Ma（University of California, Riverside）
Time: 14:30-16:00, June21
Location: Room 201，Ming De Building
Abstract: A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most existing methods require specifying certain parametric models involving the outcome, treatment and confounding variables, and employ a variable selection procedure to identify confounders. However, selection of a proper set of confounders depends on correct specification of the working models. The bias due to model misspecification and incorrect selection of confounding variables can yield misleading results. In this talk, I will talk about a robust and efficient approach we have proposed for inference about the average treatment effect via a flexible modeling strategy incorporating penalized variable selection. Specifically, we consider an estimator constructed based on an efficient influence function that involves a propensity score and an outcome regression. We then propose a new sparse sufficient dimension reduction method to estimate these two functions without making restrictive parametric modeling assumptions. The proposed estimator of the average treatment effect is asymptotically normal and semi-parametrically efficient without the need for variable selection consistency. In the end, I will talk about simulation studies and a biomedical application. This talk is based on the paper Ma, Zhu, Zhang, Tsai and Carroll (2018, AoS).
