Estimation of Causal Effects with a Binary Treatment Variable: A Unified M-Estimation Framework

Abstract

Abstract In this paper, we review several estimators of the average treatment effect (ATE) that belong to three main groups: regression, weighting and doubly robust methods. We unify the exposition of these estimators within an M-estimation framework and we derive their variance estimators from the sandwich form variance-covariance matrix of the M-Estimator. Additionally, we re-estimate the causal return to higher education on earnings by the reviewed methods using the rich dataset provided by the British National Child Development Study (NCDS) as an empirical illustration.