Alex Poirier - Georgetown University

"Sensitivity Analysis with Endogenous Controls” joint work w Matt Masten and Paul Diegert


Omitted variables are one of the most important threats to the identification of causal effects. Several widely used methods, including Oster (2019), have been developed to assess the impact of omitted variables on empirical conclusions. These methods all require an exogenous controls assumption: the omitted variables must be uncorrelated with the included controls. This is often considered a strong and implausible assumption. We provide an alternative approach to sensitivity analysis which allows for endogenous controls, while still letting researchers calibrate sensitivity parameters by comparing the magnitude of selection on observables with the magnitude of selection on unobservables. We illustrate our results in an empirical application to the effect of historical American frontier life on modern cultural beliefs. Finally, we implement these methods in a companion Stata module for easy use in practice.

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