Assessing Sensitivity to Unconfoundedness: Estimation and Inference

Matthew Masten, Alexandre Poirier and Linqi Zhang

Journal of Business & Economic Statistics, 2024, vol. 42, issue 1, 1-13

Abstract: This article provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption. Specifically, we estimate and do inference on bounds for various treatment effect parameters, like the Average Treatment Effect (ATE) and the average effect of treatment on the treated (ATT), under nonparametric relaxations of the unconfoundedness assumption indexed by a scalar sensitivity parameter c. These relaxations allow for limited selection on unobservables, depending on the value of c. For large enough c, these bounds equal the no assumptions bounds. Using a nonstandard bootstrap method, we show how to construct confidence bands for these bound functions which are uniform over all values of c. We illustrate these methods with an empirical application to the National Supported Work Demonstration program. We implement these methods in the companion Stata module tesensitivity for easy use in practice.

Date: 2024
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Assessing Sensitivity to Unconfoundedness: Estimation and Inference (2021)
Working Paper: Assessing Sensitivity to Unconfoundedness: Estimation and Inference (2020)
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DOI: 10.1080/07350015.2023.2183212

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