Estimating Heterogeneous Treatment Effects Within Latent Class Multilevel Models: A Bayesian Approach

Weicong Lyu, Jee-Seon Kim and Youmi Suk
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Jee-Seon Kim: University of Wisconsin-Madison
Youmi Suk: University of Virginia

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 1, 3-36

Abstract: This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and outcome models so that misclassification does not obstruct estimation of treatment effects. Simulation demonstrates that the proposed method finds the correct number of latent classes, estimates class-specific treatment effects well, and provides proper posterior standard deviations and credible intervals of ATEs. We apply this method to Trends in International Mathematics and Science Study data to investigate the effects of private science lessons on achievement scores and then find two latent classes, one with zero ATE and the other with positive ATE.

Keywords: causal inference; clustered data; finite mixture models; latent subgroups; Bayesian joint estimation; double robustness; TIMSS science data (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:1:p:3-36

DOI: 10.3102/10769986221115446

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