Sensitivity Analysis for Marginal Structural Models
Methodology
2022-10-12 v2 Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.2210.04681,
title = {Sensitivity Analysis for Marginal Structural Models},
author = {Matteo Bonvini and Edward Kennedy and Valerie Ventura and Larry Wasserman},
journal= {arXiv preprint arXiv:2210.04681},
year = {2022}
}