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

Randomized controlled trials (RCTs) are the gold standard for causalinference, but they are often powered only for average effects, makingestimation of heterogeneous treatment effects (HTEs) challenging. Conversely,large-scale observational studies (OS) offer a wealth of data but suffer fromconfounding bias. Our paper presents a novel framework to leverage OS data forenhancing the efficiency in estimating conditional average treatment effects(CATEs) from RCTs while mitigating common biases. We propose an innovativeapproach to combine RCTs and OS data, expanding the traditionally used controlarms from external sources. The framework relaxes the typical assumption ofCATE invariance across populations, acknowledging the often unaccountedsystematic differences between RCT and OS participants. We demonstrate thisthrough the special case of a linear outcome model, where the CATE is sparselydifferent between the two populations. The core of our framework relies onlearning potential outcome means from OS data and using them as a nuisanceparameter in CATE estimation from RCT data. We further illustrate throughexperiments that using OS findings reduces the variance of the estimated CATEfrom RCTs and can decrease the required sample size for detecting HTEs.

Leveraging Observational Data for Efficient CATE Estimation in Randomized Controlled Trials