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

Published online by Cambridge University Press:  01 January 2025

Youmi Suk Open the ORCID record for Youmi Suk [Opens in a new window]  and
Hyunseung Kang
Youmi Suk*
Affiliation:
University of Virginia
Hyunseung Kang
Affiliation:
University of Wisconsin-Madison
*
Correspondence should be made to Youmi Suk, School of Data Science, University of Virginia, 31 Bonnycastle Dr, Charlottesville, VA 22903, USA. Email: eub6uw@virginia.edu
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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.

Keywords

causal inferencemachine learning methodsunmeasured variablesomitted variable biasfixed effects models
Type
Theory and Methods
Information
Psychometrika , Volume 87 , Issue 1: Special Issue on Network Psychometrics in Action , March 2022 , pp. 310 - 343
Copyright
Copyright © 2021 The Psychometric Society

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Footnotes

Supplementary Information the online version supplementary material available at https://doi.org/10.1007/s11336-021-09805-x.

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