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/

Zendy Plus

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

Zendy Tools

Zendy Open

We consider the problem of structure learning for bow-free acyclic pathdiagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAGmodels that allow for certain hidden variables. We present a first method forthis problem using a greedy score-based search algorithm. We also prove somenecessary and some sufficient conditions for distributional equivalence of BAPswhich are used in an algorithmic ap- proach to compute (nearly) equivalentmodel structures. This allows us to infer lower bounds of causal effects. Wealso present applications to real and simulated datasets using our publiclyavailable R-package.

Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams