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Activity Number: 168 - Causal Inference
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #327230
Title: Multiply Robust Estimation of Causal Quantile Treatment Effects
Author(s): Yuying Xie* and Cecilia Cotton and Yeying Zhu
Companies: University of Waterloo and University of Waterloo and University of Waterloo
Keywords: Causal Inference; Empirical likelihood; Covariate balancing; Quantile treatment effect

An important goal in estimating the causal effect is to achieve balance in the covariates. In addition to estimating the average treatment effect or average treatment effect in the treated other quantities such as quantiles or the quantile treatment effect may be of interest. We propose a multiply robust method for estimating marginal quantiles of potential outcomes by achieving mean balance in (1) the propensity score, and (2) the conditional distributions of potential outcomes. An empirical likelihood or entropy measure can be utilized instead of using inverse probability weighting. Simulation studies are conducted across different scenarios of correctness in both the propensity score models and outcome models. Our estimator is consistent if any of the models are correctly specified.

Authors who are presenting talks have a * after their name.

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