A fundamental measure of treatment effect heterogeneity

Levy Jonathan (), Mark van der Laan (), Hubbard Alan () and Pirracchio Romain ()
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Levy Jonathan: UC Berkeley School of Public Health, University of California, Berkeley, California, United States of America
Mark van der Laan: UC Berkeley School of Public Health, University of California, Berkeley, California, United States of America
Hubbard Alan: UC Berkeley School of Public Health, University of California, Berkeley, California, United States of America
Pirracchio Romain: University of California San Francisco, ZSFG Anesthesia and Perioperative Care, San Francisco, CA, United States of America

Journal of Causal Inference, 2021, vol. 9, issue 1, 83-108

Abstract: The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment. In addition to the ATE, the variance of the stratum-specific treatment effect function is fundamental in determining the heterogeneity of treatment effect values. We offer a non-parametric plug-in estimator, the targeted maximum likelihood estimator (TMLE) and the cross-validated TMLE (CV-TMLE), to simultaneously estimate both the average and variance of the stratum-specific treatment effect function. The CV-TMLE is preferable because it guarantees asymptotic efficiency under two conditions without needing entropy conditions on the initial fits of the outcome model and treatment mechanism, as required by TMLE. Particularly, in circumstances where data adaptive fitting methods are very important to eliminate bias but hold no guarantee of satisfying the entropy condition, we show that the CV-TMLE sampling distributions maintain normality with a lower mean squared error than TMLE. In addition to verifying the theoretical properties of TMLE and CV-TMLE through simulations, we highlight some of the challenges in estimating the variance of the treatment effect, which lack double robustness and might be biased if the true variance is small and sample size insufficient.

Keywords: TMLE; targeted learning; effect modification; heterogeneity (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:9:y:2021:i:1:p:83-108:n:4

DOI: 10.1515/jci-2019-0003

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