Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing

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• Kevin Guo, Stanford University
• Thursday 11 March 2021, 16:00-17:30
• https://zoom.us/j/97791179955?pwd=eFMrTFNSRmhnTzBHdDBLZlgrTFRQdz09.

If you have a question about this talk, please contact Qingyuan Zhao.

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.

https://arxiv.org/abs/2102.04543

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