The Finite Sample Performance of Treatment Effects Estimators based on the Lasso

TL;DR

This paper evaluates the finite sample performance of treatment effect estimators based on the Lasso, comparing various methods through theory and simulations, and introduces a new robust doubly robust Kernel matching estimator.

Contribution

It provides a comprehensive comparison of Lasso-based treatment effect estimators and proposes a novel doubly robust Kernel matching estimator for improved robustness.

Findings

Alternative weighting schemes perform well in finite samples.

The proposed doubly robust Kernel matching estimator shows robustness to nuisance parameter misspecification.

Simulation results confirm theoretical advantages of certain estimators.

Abstract

This paper contributes to the literature on treatment effects estimation with machine learning inspired methods by studying the performance of different estimators based on the Lasso. Building on recent work in the field of high-dimensional statistics, we use the semiparametric efficient score estimation structure to compare different estimators. Alternative weighting schemes are considered and their suitability for the incorporation of machine learning estimators is assessed using theoretical arguments and various Monte Carlo experiments. Additionally we propose an own estimator based on doubly robust Kernel matching that is argued to be more robust to nuisance parameter misspecification. In the simulation study we verify theory based intuition and find good finite sample properties of alternative weighting scheme estimators like the one we propose.