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Sensitivity Analysis for Marginal Structural Models

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arxiv 2210.04681 v2 pith:PDJR25T2 submitted 2022-10-10 stat.ME math.STstat.TH

Matteo Bonvini , Edward Kennedy , Valerie Ventura , Larry Wasserman This is my paper · ORCID
classification stat.MEmath.STstat.TH
keywords confoundingmodelmodelssensitivitymarginalstructuralunmeasuredallow
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We introduce several methods for assessing sensitivity to unmeasured confounding in marginal structural models; importantly we allow treatments to be discrete or continuous, static or time-varying. We consider three sensitivity models: a propensity-based model, an outcome-based model, and a subset confounding model, in which only a fraction of the population is subject to unmeasured confounding. In each case we develop efficient estimators and confidence intervals for bounds on the causal parameters.

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Cited by 1 Pith paper

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  1. Stochastic interventions, sensitivity analysis, and optimal transport

    stat.ME 2024-11 accept novelty 8.0 of 10

    Generalized treatment policies built from optimal transport couplings resolve the non-collapsing bound problem and give sharp, efficient sensitivity bounds for stochastic interventions.

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