The Performance of Largest Caliper Matching: A Monte Carlo Simulation Approach

TL;DR

This paper introduces a new 'largest caliper matching' method for treatment effect estimation, compares it with existing methods through simulations and real data, and finds it improves bias reduction and efficiency in various settings.

Contribution

The paper proposes a novel, computationally efficient matching method and evaluates its performance against existing methods via Monte Carlo simulations and real data analysis.

Findings

Largest caliper matching reduces bias effectively.

Matched samples improve treatment effect estimation.

Performance varies with data characteristics and treatment prevalence.

Abstract

The paper presents an investigation of estimating treatment effect using different matching methods. The study proposed a new method which is computationally efficient and convenient in implication-'largest caliper matching' and compared the performance with other five popular matching methods by simulation. The bias, empirical standard deviation and the mean square error of the estimates in the simulation are checked under different treatment prevalence and different distributions of covariates. A Monte Carlo simulation study and a real data example are employed to measure the performance of these methods. It is shown that matched samples improve estimation of the population treatment effect in a wide range of settings. It reduces the bias if the data contains the selection on observables and treatment imbalances. Also, findings about the relative performance of the different matching methods are provided to help practitioners determine which method should be used under certain situations.