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Robust Estimation of Inverse Probability Weights for Marginal Structural Models

Author(s): Imai, Kosuke; Ratkovic, Marc

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dc.contributor.authorImai, Kosuke-
dc.contributor.authorRatkovic, Marc-
dc.date.accessioned2020-02-19T21:21:29Z-
dc.date.available2020-02-19T21:21:29Z-
dc.date.issued2015-07-03en_US
dc.identifier.citationImai, K, Ratkovic, M. (2015). Robust Estimation of Inverse Probability Weights for Marginal Structural Models. Journal of the American Statistical Association, 110 (511), 1013 - 1023. doi:10.1080/01621459.2014.956872en_US
dc.identifier.issn0162-1459-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1zj43-
dc.description.abstract© 2015, © American Statistical Association. Marginal structural models (MSMs) are becoming increasingly popular as a tool for causal inference from longitudinal data. Unlike standard regression models, MSMs can adjust for time-dependent observed confounders while avoiding the bias due to the direct adjustment for covariates affected by the treatment. Despite their theoretical appeal, a main practical difficulty of MSMs is the required estimation of inverse probability weights. Previous studies have found that MSMs can be highly sensitive to misspecification of treatment assignment model even when the number of time periods is moderate. To address this problem, we generalize the covariate balancing propensity score (CBPS) methodology of Imai and Ratkovic to longitudinal analysis settings. The CBPS estimates the inverse probability weights such that the resulting covariate balance is improved. Unlike the standard approach, the proposed methodology incorporates all covariate balancing conditions across multiple time periods. Since the number of these conditions grows exponentially as the number of time period increases, we also propose a low-rank approximation to ease the computational burden. Our simulation and empirical studies suggest that the CBPS significantly improves the empirical performance of MSMs by making the treatment assignment model more robust to misspecification. Open-source software is available for implementing the proposed methods.en_US
dc.format.extent1 - 30en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of the American Statistical Associationen_US
dc.rightsAuthor's manuscripten_US
dc.titleRobust Estimation of Inverse Probability Weights for Marginal Structural Modelsen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1080/01621459.2014.956872-
dc.identifier.eissn1537-274X-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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