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

Unobserved confounding presents a major threat to causal inference fromobservational studies. Recently, several authors suggest that this problem maybe overcome in a shared confounding setting where multiple treatments areindependent given a common latent confounder. It has been shown that under alinear Gaussian model for the treatments, the causal effect is not identifiablewithout parametric assumptions on the outcome model. In this paper, we showthat the causal effect is indeed identifiable if we assume a general binarychoice model for the outcome with a non-probit link. Our identificationapproach is based on the incongruence between Gaussianity of the treatments andlatent confounder, and non-Gaussianity of a latent outcome variable. We furtherdevelop a two-step likelihood-based estimation procedure.

Identifiability of causal effects with multiple causes and a binary outcome