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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2022

Open Access 01-12-2022 | Research

Estimation of average treatment effect based on a multi-index propensity score

Authors: Jiaqin Xu, Kecheng Wei, Ce Wang, Chen Huang, Yaxin Xue, Rui Zhang, Guoyou Qin, Yongfu Yu

Published in: BMC Medical Research Methodology | Issue 1/2022

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Abstract

Background

Estimating the average effect of a treatment, exposure, or intervention on health outcomes is a primary aim of many medical studies. However, unbalanced covariates between groups can lead to confounding bias when using observational data to estimate the average treatment effect (ATE). In this study, we proposed an estimator to correct confounding bias and provide multiple protection for estimation consistency.

Methods

With reference to the kernel function-based double-index propensity score (Ker.DiPS) estimator, we proposed the artificial neural network-based multi-index propensity score (ANN.MiPS) estimator. The ANN.MiPS estimator employed the artificial neural network to estimate the MiPS that combines the information from multiple candidate models for propensity score and outcome regression. A Monte Carlo simulation study was designed to evaluate the performance of the proposed ANN.MiPS estimator. Furthermore, we applied our estimator to real data to discuss its practicability.

Results

The simulation study showed the bias of the ANN.MiPS estimators is very small and the standard error is similar if any one of the candidate models is correctly specified under all evaluated sample sizes, treatment rates, and covariate types. Compared to the kernel function-based estimator, the ANN.MiPS estimator usually yields smaller standard error when the correct model is incorporated in the estimator. The empirical study indicated the point estimation for ATE and its bootstrap standard error of the ANN.MiPS estimator is stable under different model specifications.

Conclusions

The proposed estimator extended the combination of information from two models to multiple models and achieved multiply robust estimation for ATE. Extra efficiency was gained by our estimator compared to the kernel-based estimator. The proposed estimator provided a novel approach for estimating the causal effects in observational studies.
Appendix
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Metadata
Title
Estimation of average treatment effect based on a multi-index propensity score
Authors
Jiaqin Xu
Kecheng Wei
Ce Wang
Chen Huang
Yaxin Xue
Rui Zhang
Guoyou Qin
Yongfu Yu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2022
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-022-01822-3

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