A Single Iterative Step for Anytime Causal Discovery

We present a sound and complete algorithm for recovering causal graphs from observed, non-interventional data, in the possible presence of latent confounders and selection bias. We rely on the causal Markov and faithfulness assumptions and recover the equivalence class of the underlying causal graph...

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Bibliographische Detailangaben

Hauptverfasser:	Rohekar, Raanan Y, Gurwicz, Yaniv, Nisimov, Shami, Novik, Gal
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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