Identification of Treatment Effects under Conditional Partial Independence

Abstract

Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.

Keywords: Treatment Effects, Conditional Independence, Unconfoundedness, Selection on Observables, Sensitivity Analysis, Nonparametric Identification, Partial Identification

JEL Classification: C14, C18, C21, C51