Sensitivity Analysis for Marginal Structural Models

TL;DR

This paper presents new methods for sensitivity analysis in marginal structural models, accommodating various treatment types and confounding scenarios, with efficient estimators and confidence intervals for causal bounds.

Contribution

It introduces flexible sensitivity models and estimators for unmeasured confounding in marginal structural models, applicable to discrete, continuous, static, and time-varying treatments.

Findings

Developed propensity-based, outcome-based, and subset confounding sensitivity models.

Provided efficient estimators and confidence intervals for causal bounds.

Applicable to a wide range of treatment and confounding scenarios.

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.