Identification of Treatment Effects Under Conditional Partial Independence
在条件独立假设不成立时，通过非参数偏离来识别处理效应参数，帮助评估实证结论的稳健性。
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.