A1111
Title: Rematching estimators for average treatment effects
Authors: Lam Lam Hui - The Chinese University of Hong Kong (Hong Kong) [presenting]
Kin Wai Chan - The Chinese University of Hong Kong (Hong Kong)
Abstract: Matching estimators are widely applied in practice for their great intuitive appeal. However, simple matching estimators with a fixed number of matches ($M_0$) are generally inefficient. Matching estimators with a variable number of matches are proposed to gain efficiency via rematching. Rather than increasing $M_0$ to gain precision, which introduces a substantial increase in bias, the key is to rematch the treated units from the opposite direction to utilize unmatched control units. The proposed rematching estimators are applicable to both the average treatment effect and its counterpart for the treated population. They are proven asymptotically valid and uniformly more efficient than matching estimators. Simulation results confirm that the proposed rematching estimators substantially improve the simple matching estimators in finite samples. As an empirical illustration, we apply the estimators proposed in this article to the National Supported Work data.
