English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Motivated by applications in personalized medicine and individualized policymaking, there is a growing interest in techniques for quantifying treatmenteffect heterogeneity in terms of the conditional average treatment effect(CATE). Some of the most prominent methods for CATE estimation developed inrecent years are T-Learner, DR-Learner and R-Learner. The latter two weredesigned to improve on the former by being Neyman-orthogonal. However, therelations between them remain unclear, and likewise does the literature remainvague on whether these learners converge to a useful quantity or (functional)estimand when the underlying optimization procedure is restricted to a class offunctions that does not include the CATE. In this article, we provide insightinto these questions by discussing DR-learner and R-learner as special cases ofa general class of Neyman-orthogonal learners for the CATE, for which wemoreover derive oracle bounds. Our results shed light on how one may constructNeyman-orthogonal learners with desirable properties, on when DR-learner may bepreferred over R-learner (and vice versa), and on novel learners that maysometimes be preferable to either of these. Theoretical findings are confirmedusing results from simulation studies on synthetic data, as well as anapplication in critical care medicine.

On a General Class of Orthogonal Learners for the Estimation of Heterogeneous Treatment Effects