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

In this paper, we introduce VACA, a novel class of variational graphautoencoders for causal inference in the absence of hidden confounders, whenonly observational data and the causal graph are available. Without making anyparametric assumptions, VACA mimics the necessary properties of a StructuralCausal Model (SCM) to provide a flexible and practical framework forapproximating interventions (do-operator) and abduction-action-predictionsteps. As a result, and as shown by our empirical results, VACA accuratelyapproximates the interventional and counterfactual distributions on diverseSCMs. Finally, we apply VACA to evaluate counterfactual fairness in fairclassification problems, as well as to learn fair classifiers withoutcompromising performance.

VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries