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Activity Number: 505 - Flexible Methods for Causality Research
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #328409
Title: Matching Using Sufficient Dimension Reduction for Causal Inference
Author(s): Yeying Zhu* and Wei Luo
Companies: University of Waterloo and Baruch College
Keywords: Causal inference; Central subspace; Common support condition; Dimension reduction; Matching ; Sliced inverse regression

To estimate casual treatment effects, we propose a new matching approach based on the reduced covariates obtained from sufficient dimension reduction. Compared to the original covariates and the propensity score, which are commonly used for matching in the literature, the reduced covariates are estimable nonparametrically under a mild assumption on the original covariates, and are sufficient and effective in imputing the missing potential outcomes. In addition, under the ignorability assumption, the consistency of the proposed approach requires a weaker common support condition, and the researchers are allowed to use different reduced covariates to find matched subjects for different treatment groups. We develop relative asymptotic results, and conduct real data analysis to illustrate the usefulness of the proposed approach.

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

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