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

Author

Listed:
  • Weicong Lyu
  • Jee-Seon Kim

    (University of Wisconsin-Madison)

  • Youmi Suk

    (University of Virginia)

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.

Suggested Citation

  • Weicong Lyu & Jee-Seon Kim & Youmi Suk, 2023. "Estimating Heterogeneous Treatment Effects Within Latent Class Multilevel Models: A Bayesian Approach," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 3-36, February.
    • Handle: RePEc:sae:jedbes:v:48:y:2023:i:1:p:3-36
    DOI: 10.3102/10769986221115446
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    References listed on IDEAS

    as
    1. Imai, Kosuke & Strauss, Aaron, 2011. "Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign," Political Analysis, Cambridge University Press, vol. 19(1), pages 1-19, January.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    4. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, Enero-Abr.
    6. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
    7. Hong, Guanglei & Raudenbush, Stephen W., 2006. "Evaluating Kindergarten Retention Policy: A Case Study of Causal Inference for Multilevel Observational Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 901-910, September.
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    Cited by:

    1. Joshua B. Gilbert & Luke W. Miratrix & Mridul Joshi & Benjamin W. Domingue, 2025. "Disentangling Person-Dependent and Item-Dependent Causal Effects: Applications of Item Response Theory to the Estimation of Treatment Effect Heterogeneity," Journal of Educational and Behavioral Statistics, , vol. 50(1), pages 72-101, February.

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