EcoSta 2022: Start Registration
View Submission - EcoSta2022
A0175
Title: Multiply robust estimation of quantile treatment effects with missing responses Authors:  Yanlin Tang - East China Normal University (China) [presenting]
Abstract: Causal inference and missing data have attracted significant research interests in recent years, while the current literature usually focuses on only one of these two issues. Moreover, compared with the commonly used average treatment effect (ATE), the quantile treatment effect (QTE) is able to provide a complete picture of the difference between the treatment and control groups, as well as robustness to the outliers in the responses. Therefore, we develop a method to estimate the QTE in the context of missing data based on the idea of inverse probability weighting (IPW). The proposed IPW estimator has the property of multiply robustness, that is, as long as the class of candidate models of propensity scores contains the correct model and so does the candidate models for the probability of being observed, the resulting QTE estimator is root-n consistent and asymptotic normal. Simulation studies are conducted to investigate the performance of the proposed method, and real data from CHARLS is analyzed and different treatment effects are observed at various quantile levels of the response.