Identification of Treatment Effects under Conditional Partial Independence
Methodology
2017-10-25 v1 Econometrics
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.
Cite
@article{arxiv.1707.09563,
title = {Identification of Treatment Effects under Conditional Partial Independence},
author = {Matthew A. Masten and Alexandre Poirier},
journal= {arXiv preprint arXiv:1707.09563},
year = {2017}
}