Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing
Abstract
Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from observational data. However, its correctness relies on the untestable (and frequently implausible) assumption that all confounders have been measured. This paper introduces a robust sensitivity analysis for IPW that estimates the range of treatment effects compatible with a given amount of unobserved confounding. The estimated range converges to the narrowest possible interval (under the given assumptions) that must contain the true treatment effect. Our proposal is a refinement of the influential sensitivity analysis by Zhao, Small, and Bhattacharya (2019), which we show gives bounds that are too wide even asymptotically. This analysis is based on new partial identification results for Tan (2006)'s marginal sensitivity model.
Cite
@article{arxiv.2102.04543,
title = {Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing},
author = {Jacob Dorn and Kevin Guo},
journal= {arXiv preprint arXiv:2102.04543},
year = {2023}
}
Comments
This is an original manuscript of an article published by Taylor & Francis in the Journal of the American Statistical Association in 2022, available online: https://doi.org/10.1080/01621459.2022.2069572