Identification of Treatment Effects Under Conditional Partial Independence

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.

• Publication year

  2017

• Venue

  Econometrica

• Publication date

  2017-07-29

• Fields of study

  Mathematics, Economics

• Identifiers
  DOI 10.3982/ECTA14481arXiv 1707.09563
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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