When causal inference meets deep learning

Bayesian networks can capture causal relations, but learning such a network from data is NP-hard. Recent work has made it possible to approximate this problem as a continuous optimization task that can be solved efficiently with well-established numerical techniques.

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Bibliographische Detailangaben
Veröffentlicht in:	Nature machine intelligence 2020-08, Vol.2 (8), p.426-427
Hauptverfasser:	Luo, Yunan, Peng, Jian, Ma, Jianzhu
Format:	Artikel
Sprache:	eng
Schlagworte:	38/39 45 631/114/1305 631/114/2164 Accuracy Algorithms Artificial intelligence Bayesian analysis Causality Deep learning Engineering Machine learning Methods Neural networks News & Views news-and-views Optimization Random variables
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	Bayesian networks can capture causal relations, but learning such a network from data is NP-hard. Recent work has made it possible to approximate this problem as a continuous optimization task that can be solved efficiently with well-established numerical techniques.
ISSN:	2522-5839 2522-5839
DOI:	10.1038/s42256-020-0218-x