Bayesian index models for heterogeneous treatment effects

2022/05/03 by Hyung Park, Park, Hyung, Danni Wu +7
Mathematics · #Advanced Causal Inference Techniques #COVID-19 epidemiological studies #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.2205.01795

openalex publication_date 2022/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The general idea of this article is to develop a Bayesian model with a flexible link function connecting an exponential family treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models," and among popular semi-parametric modeling methods. In this article, we will focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.

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