Sensitivity Analysis for Marginal Structural Models

2022/10/10 by Matteo Bonvini, Edward M. Kennedy, Bonvini, Matteo +5 · 4 citations
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #FOS: Mathematics #Health Systems, Economic Evaluations, Quality of Life #Methodology (stat.ME) #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2210.04681

openalex publication_date 2022/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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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