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.
MLA
Masten, Matthew A., and Alexandre Poirier. “Identification of Treatment Effects under Conditional Partial Independence.” Econometrica, vol. 86, .no 1, Econometric Society, 2018, pp. 317-351, https://doi.org/10.3982/ECTA14481
Chicago
Masten, Matthew A., and Alexandre Poirier. “Identification of Treatment Effects under Conditional Partial Independence.” Econometrica, 86, .no 1, (Econometric Society: 2018), 317-351. https://doi.org/10.3982/ECTA14481
APA
Masten, M. A., & Poirier, A. (2018). Identification of Treatment Effects under Conditional Partial Independence. Econometrica, 86(1), 317-351. https://doi.org/10.3982/ECTA14481
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