Limit theorems of matching estimators with a fixed number of matches

TL;DR

This paper establishes a new non-normalized CLT for fixed-matches matching estimators, explicitly calculating the limiting variance and resolving key theoretical gaps in prior work.

Contribution

It provides the first non-normalized CLT with an explicit limiting variance for matching estimators with fixed matches, addressing previous unresolved theoretical issues.

Findings

Proved convergence of the normalizing statistic to its mean.

Derived a closed-form expression for the limit of the mean.

Resolved gaps in the theoretical foundation of matching estimators.

Abstract

This paper re-examines the limit theorems of Abadie and Imbens for nearest-neighbor matching estimators of average treatment effects with a fixed number of matches. We establish, for the first time, a non-normalized central limit theorem (CLT) with an explicitly calculated limiting variance. The key ingredients are to prove the convergence of the normalizing statistic appearing in the CLT of Abadie and Imbens to its mean, and to calculate the closed form of the limit of this mean. The former closes a gap in the argument of an unpublished work (Abadie and Imbens, 2002), while the latter resolves a question raised in Abadie and Imbens (2006).