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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study counterfactual identifiability in causal models with bijectivegeneration mechanisms (BGM), a class that generalizes several widely-usedcausal models in the literature. We establish their counterfactualidentifiability for three common causal structures with unobserved confounding,and propose a practical learning method that casts learning a BGM as structuredgenerative modeling. Learned BGMs enable efficient counterfactual estimationand can be obtained using a variety of deep conditional generative models. Weevaluate our techniques in a visual task and demonstrate its application in areal-world video streaming simulation task.

Counterfactual Identifiability of Bijective Causal Models