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Sensitivity analysis of individual treatment effects: A robust conformal inference approach by Ying Jin; Zhimei Ren; Emmanuel J. Candès is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Sensitivity analysis of individual treatment effects: A robust conformal inference approach about?

## Significance The individual treatment effect (ITE) describes the difference between an individual’s outcome when receiving a treatment versus not. This difference may vary across individuals conditional on their characteristics. In observational studies, inference for ITEs can be invalid if one ignores unmeasured confounding factors that simultaneously influence the treatment assignment and the outcomes. We propose a framework to quantitatively understand the robustness of causal conclusions on ITEs against such potential confounding factors. This yields prediction bands, which come with rigorous uncertainty quantification tools. These tools apply regardless of the machine learning model employed to learn the treatment effect, however complicated, and regardless of the sample size.

Who reads Sensitivity analysis of individual treatment effects: A robust conformal inference approach?

Author

Ying Jin; Zhimei Ren; Emmanuel J. Candès

Publisher

Proceedings of the National Academy of Sciences

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)