Simultaneous treatment effect estimation and variable selection for observational data

Abstract

Due to the difficulties inherent in conducting controlled experiments, recent causal inference studies have focused on developing treatment effect estimation using observational data. One major difficulty in causal inference from observational data is that the underlying causal structure is unknown. This may result in the misidentification of potential sources of causal estimation bias, such as confounders, which must be controlled for accurate estimation. To consider all possible confounders and other relevant information, conventional methods predominantly use all observed covariates indiscriminately. However, previous studies have shown that including all covariates without considering their causal relationships may deteriorate the estimation performance. Although several data-driven variable selection methods have been proposed for treatment effect estimation, they cannot distinguish the confounders from other outcome-related covariates and are limited to simple regression forms. In this study, we propose a method called the Variable Selection Causal Estimation Network (VSCEN) that performs treatment effect estimation and causal variable selection simultaneously. Through end-to-end differentiable training, the VSCEN selects only the covariates beneficial for effect estimation and uses only those selected for effect estimation. Experimental evaluations on fully synthetic, semi-synthetic, and real datasets demonstrate the VSCEN's superior performance in conditional average treatment effect estimation and competitive performance in average treatment effect estimation along with its accurate variable selection capabilities.