Optimal balancing of time-dependent confounders for marginal structural models

Kallus Nathan () and Santacatterina Michele ()
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Kallus Nathan: Department of Operations Research and Information Engineering and Cornell Tech, Cornell University, New York 10044, New York, USA
Santacatterina Michele: Department of Population Health, New York University Grossman School of Medicine, New York 10016, New York, USA

Journal of Causal Inference, 2021, vol. 9, issue 1, 345-369

Abstract: Marginal structural models (MSMs) can be used to estimate the causal effect of a potentially time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can be sensitive to model misspecification and lead to high-variance estimates due to extreme weights. Various methods have been proposed to partially address this, including covariate balancing propensity score (CBPS) to mitigate treatment model misspecification, and truncation and stabilized-IPTW (sIPTW) to temper extreme weights. In this article, we present kernel optimal weighting (KOW), a convex-optimization-based approach that finds weights for fitting the MSMs that flexibly balance time-dependent confounders while simultaneously penalizing extreme weights, directly addressing the above limitations. We further extend KOW to control for informative censoring. We evaluate the performance of KOW in a simulation study, comparing it with IPTW, sIPTW, and CBPS. We demonstrate the use of KOW in studying the effect of treatment initiation on time-to-death among people living with human immunodeficiency virus and the effect of negative advertising on elections in the United States.

Keywords: causal inference; optimization; covariate balance; time-dependent treatments; marginal structural models (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:345-369:n:8

DOI: 10.1515/jci-2020-0033

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