Nonparametric inference for interventional effects with multiple mediators

Benkeser David () and Ran Jialu
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Benkeser David: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Atlanta, USA
Ran Jialu: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Atlanta, USA

Journal of Causal Inference, 2021, vol. 9, issue 1, 172-189

Abstract: Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estimators of these effects are based on parametric models with confidence interval estimation facilitated via the nonparametric bootstrap. We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques to be considered. In particular, we establish weak convergence results that facilitate the construction of closed-form confidence intervals and hypothesis tests and prove multiple robustness properties of the proposed estimators. Simulations show that inference based on large-sample theory has adequate small-sample performance. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.

Keywords: mediation; causal inference; augmented inverse probability of treatment weighted estimator; targeted minimum loss estimator; machine learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:9:y:2021:i:1:p:172-189:n:2

DOI: 10.1515/jci-2020-0018

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