Estimation of Treatment Effects for Heterogeneous Matched Pairs Data with Probit Models

TL;DR

This paper introduces new estimators for treatment effects in heterogeneous matched pairs data, demonstrating their advantages over existing methods through simulations and real data applications.

Contribution

The paper proposes novel estimators for treatment effects in heterogeneous matched pairs data, with proven asymptotic properties and improved performance over traditional estimators.

Findings

Proposed estimators outperform Heckman's and IPW estimators in simulations.

New estimators have desirable asymptotic properties.

Applied methods to medical datasets, demonstrating practical utility.

Abstract

Estimating the effect of medical treatments on subject responses is one of the crucial problems in medical research. Matched-pairs designs are commonly implemented in the field of medical research to eliminate confounding and improve efficiency. In this article, new estimators of treatment effects for heterogeneous matched pairs data are proposed. Asymptotic properties of the proposed estimators are derived. Simulation studies show that the proposed estimators have some advantages over the famous Heckman's estimator and inverse probability weighted (IPW) estimator. We apply the proposed methodologies to a blood lead level data set and an acute leukaemia data set.