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Optimal balancing of time-dependent confounders for marginal structural models

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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)

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.

Suggested Citation

  • Kallus Nathan & Santacatterina Michele, 2021. "Optimal balancing of time-dependent confounders for marginal structural models," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 345-369, January.
    • Handle: RePEc:bpj:causin:v:9:y:2021:i:1:p:345-369:n:8
    DOI: 10.1515/jci-2020-0033
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    References listed on IDEAS

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    3. José R. Zubizarreta, 2015. "Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 910-922, September.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    5. van der Wal, Willem M. & Geskus, Ronald B., 2011. "ipw: An R Package for Inverse Probability Weighting," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i13).
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