A Semiparametric Approach to Model-Based Sensitivity Analysis in Observational Studies.

ABSTRACT: When drawing causal inference from observational data, there is almost always concern about unmeasured confounding. One way to tackle this is to conduct a sensitivity analysis. One widely-used sensitivity analysis framework hypothesizes the existence of a scalar unmeasured confounder U and asks how the causal conclusion would change were U measured and included in the primary analysis. Work along this line often makes various parametric assumptions on U, for the sake of mathematical and computational convenience. In this article, we further this line of research by developing a valid sensitivity analysis that leaves the distribution of U unrestricted. Compared to many existing methods in the literature, our method allows for a larger and more flexible family of models, mitigates observable

SUBMITTER: Zhang B 

PROVIDER: S-EPMC9916707 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature