Incremental intervention effects in studies with dropout and many timepoints#

Kim Kwangho (), Kennedy Edward H. () and Naimi Ashley I. ()
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Kim Kwangho: Department of Health Care Policy, Harvard Medical School, Boston, MA, United States of America
Kennedy Edward H.: Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, United States of America
Naimi Ashley I.: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, United States of America

Journal of Causal Inference, 2021, vol. 9, issue 1, 302-344

Abstract: Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set treatment values deterministically) to accommodate multiple outcomes and subject dropout. We give an identifying expression for incremental intervention effects when dropout is conditionally ignorable (without requiring treatment positivity) and derive the nonparametric efficiency bound for estimating such effects. Then we present efficient nonparametric estimators, showing that they converge at fast parametric rates and yield uniform inferential guarantees, even when nuisance functions are estimated flexibly at slower rates. We also study the variance ratio of incremental intervention effects relative to more conventional deterministic effects in a novel infinite time horizon setting, where the number of timepoints can grow with sample size and show that incremental intervention effects yield near-exponential gains in statistical precision in this setup. Finally, we conclude with simulations and apply our methods in a study of the effect of low-dose aspirin on pregnancy outcomes.

Keywords: causal inference; time-varying confounding; right-censoring; longitudinal study; positivity (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:302-344:n:13

DOI: 10.1515/jci-2020-0031

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