Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data

no code implementations • 15 Dec 2019 • Steve Yadlowsky, Fabio Pellegrini, Federica Lionetto, Stefan Braune, Lu Tian

Motivated by the need of modeling the number of relapses in multiple sclerosis patients, where the ratio of relapse rates is a natural choice of the treatment effect, we propose to estimate the conditional average treatment effect (CATE) as the ratio of expected potential outcomes, and derive a doubly robust estimator of this CATE in a semiparametric model of treatment-covariate interactions.

regression