Date: 2018-10-05

Time: 15:30-16:30

Location: BURN 1104

Abstract:

In this talk, we discuss how sufficient dimension reduction can be used to aid causal inference. We propose a new matching approach based on the reduced covariates obtained from sufficient dimension reduction. Compared with the original covariates and the propensity scores, which are commonly used for matching in the literature, the reduced covariates are estimable nonparametrically and are effective in imputing the missing potential outcomes. Under the ignorability assumption, the consistency of the proposed approach requires a weaker common support condition than the one we often assume for propensity score-based methods. We develop asymptotic properties, and conduct simulation studies as well as real data analysis to illustrate the proposed approach.

Speaker

Yeying Zhu is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Her research interest lies in causal inference, machine learning and the interface between the two. Her current work focuses on the inverse weighted estimation of causal effects using propensity scores and marginal structural models.

Organized by the McGill Statistics Group

Seminar website: https://mcgillstat.github.io/