On Estimating Regression-Based Causal Effects Using Sufficient Dimension Reduction

• Activity Number: 270 - Intersection of Econometrics and Biometrics in Making Policy and Treatment Determinations
• Type: Invited
• Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Wei Luo* and Yeying Zhu and Debashis Ghosh
• Companies: Baruch College and University of Waterloo and Colorado School of Public Health
• Keywords: asymptotic efficiency ; causal inference ; central mean subspace ; common support condition ; minimum average variance estimation ; regression causal effect

Abstract

In many causal inference problems the parameter of interest is the regression causal effect, defined as the conditional mean difference in the potential outcomes given covariates. This paper discusses how sufficient dimension reduction can be used to assist causal inference, and proposes a new estimator of the regression causal effect inspired by minimum average variance estimation. The estimator requires a weaker common support condition than propensity score-based approaches, and can be used to estimate the average causal effect, for which it is shown to be asymptotically superefficient. Its finite-sample properties are illustrated by simulation.