TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS

• 成果类型: Article
• 署名作者: Shin, Heejun; Linero, Antonio; Audirac, Michelle; Irene, Kezia; Braun, Danielle; Antonelli, Joseph
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; University of Texas System; University of Texas Austin; State University System of Florida; University of Florida
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2060
• 发表日期: 2025-09
• 页码: 1847-1867
• 关键词: Causal Inference; Bayesian Nonparametrics; environmental mixtures; Treatment effect heterogeneity; variable importance measures; regression; HEALTH; inference; exposures; pollution; bart

摘要

Estimating the joint effect of a multivariate, continuous exposure is crucial, particularly in environmental health where interest lies in simultaneously evaluating the impact of multiple environmental pollutants on health. We develop novel methodology that addresses two key issues for estimation of treatment effects of multivariate, continuous exposures. We use nonparametric Bayesian methodology that is flexible to ensure our approach can capture a wide range of data generating processes. Additionally, we allow the effect of the exposures to be heterogeneous with respect to covariates. Treatment effect heterogeneity has not been well explored in the causal inference literature for multivariate, continuous exposures, and, therefore, we introduce novel estimands that summarize the nature and extent of the heterogeneity and propose estimation procedures for new estimands related to treatment effect heterogeneity. We provide theoretical support for the proposed models in the form of posterior contraction rates and show that it works well in simulated examples both with and without heterogeneity. Our approach is motivated by a study of the health effects of simultaneous exposure to the components of PM2.5, where we find that the negative health effects of exposure to environmental pollutants are exacerbated by low socioeconomic status, race and age.