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

Heterogeneity and comorbidity are two interwoven challenges associated withvarious healthcare problems that greatly hampered research on developingeffective treatment and understanding of the underlying neurobiologicalmechanism. Very few studies have been conducted to investigate heterogeneouscausal effects (HCEs) in graphical contexts due to the lack of statisticalmethods. To characterize this heterogeneity, we first conceptualizeheterogeneous causal graphs (HCGs) by generalizing the causal graphical modelwith confounder-based interactions and multiple mediators. Such confounderswith an interaction with the treatment are known as moderators. This allows usto flexibly produce HCGs given different moderators and explicitly characterizeHCEs from the treatment or potential mediators on the outcome. We establish thetheoretical forms of HCEs and derive their properties at the individual levelin both linear and nonlinear models. An interactive structural learning isdeveloped to estimate the complex HCGs and HCEs with confidence intervalsprovided. Our method is empirically justified by extensive simulations and itspractical usefulness is illustrated by exploring causality among psychiatricdisorders for trauma survivors.

On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs