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Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding

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  • Youmi Suk

    (University of Virginia)

  • Hyunseung Kang

    (University of Wisconsin-Madison)

Abstract

Recently, machine learning (ML) methods have been used in causal inference to estimate treatment effects in order to reduce concerns for model mis-specification. However, many ML methods require that all confounders are measured to consistently estimate treatment effects. In this paper, we propose a family of ML methods that estimate treatment effects in the presence of cluster-level unmeasured confounders, a type of unmeasured confounders that are shared within each cluster and are common in multilevel observational studies. We show through simulation studies that our proposed methods are robust from biases from unmeasured cluster-level confounders in a variety of multilevel observational studies. We also examine the effect of taking an algebra course on math achievement scores from the Early Childhood Longitudinal Study, a multilevel observational educational study, using our methods. The proposed methods are available in the CURobustML R package.

Suggested Citation

  • Youmi Suk & Hyunseung Kang, 2022. "Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 310-343, March.
    • Handle: RePEc:spr:psycho:v:87:y:2022:i:1:d:10.1007_s11336-021-09805-x
    DOI: 10.1007/s11336-021-09805-x
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    Cited by:

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    2. Youmi Suk & Kyung T. Han, 2024. "A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 151-172, April.

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