Propensity Score Analysis With Local Balance

Authors

Yan Li
Liang Li

Publication Date

7-10-2023

Journal

Statistics in Medicine

Abstract

Most propensity score (PS) analysis methods rely on a correctly specified parametric PS model, which may result in biased estimation of the average treatment effect (ATE) when the model is misspecified. More flexible nonparametric models for treatment assignment alleviate this issue, but they do not always guarantee covariate balance. Methods that force balance in the means of covariates and their transformations between the treatment groups, termed global balance in this article, do not always lead to unbiased estimation of ATE. Their estimated propensity scores only ensure global balance but not the balancing property, which is defined as the conditional independence between treatment assignment and covariates given the propensity score. The balancing property implies not only global balance but also local balance-the mean balance of covariates in propensity score stratified sub-populations. Local balance implies global balance, but the reverse is false. We propose the propensity score with local balance (PSLB) methodology, which incorporates nonparametric propensity score models and optimizes local balance. Extensive numerical studies showed that the proposed method can substantially outperform existing methods that estimate the propensity score by optimizing global balance, when the model is misspecified. The proposed method is implemented in the R package PSLB.

Keywords

Humans, Propensity Score, Computer Simulation, Models, Statistical, Average treatment effect, Covariate balance, Causal inference, Inverse propensity score weighting, Kernel method, Parameter tuning

DOI

10.1002/sim.9741

PMID

37012676

PMCID

PMC11390285

PubMedCentral® Posted Date

9-12-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes