Identification of Treatment Effects under Conditional Partial Independence

TL;DR

This paper develops methods to assess the robustness of treatment effect estimates when the key assumption of conditional independence is potentially violated, by deriving identified sets under deviations modeled through a conditional treatment assignment probability.

Contribution

It introduces a framework for deriving identified sets for treatment effects under nonparametric deviations from conditional independence, enhancing robustness analysis.

Findings

Provides identified sets for treatment effects under deviations

Offers a straightforward interpretation via conditional treatment probability

Enables robustness checks for empirical conclusions

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.