Article

Local causal discovery of direct causes and effects

Authors:

Qiang JiAuthors Info & Claims

NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2

Pages 2512 - 2520

Published: 07 December 2015 Publication History

Abstract

We focus on the discovery and identification of direct causes and effects of a target variable in a causal network. State-of-the-art causal learning algorithms generally need to find the global causal structures in the form of complete partial directed acyclic graphs (CPDAG) in order to identify direct causes and effects of a target variable. While these algorithms are effective, it is often unnecessary and wasteful to find the global structures when we are only interested in the local structure of one target variable (such as class labels). We propose a new local causal discovery algorithm, called Causal Markov Blanket (CMB), to identify the direct causes and effects of a target variable based on Markov Blanket Discovery. CMB is designed to conduct causal discovery among multiple variables, but focuses only on finding causal relationships between a specific target variable and other variables. Under standard assumptions, we show both theoretically and experimentally that the proposed local causal discovery algorithm can obtain the comparable identification accuracy as global methods but significantly improve their efficiency, often by more than one order of magnitude.

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Cited By

Zhang HZhou SYan CGuan JWang X(2019)Recursively learning causal structures using regression-based conditional independence testProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33013108(3108-3115)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33013108
de Carvalho WZarate LHung CPapadopoulos G(2019)Causality relationship among attributes applied in an educational data setProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297406(1271-1277)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297406
Gao TFadnis KCampbell M(2017)Local-to-global Bayesian network structure learningProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305381.3305505(1193-1202)Online publication date: 6-Aug-2017
https://dl.acm.org/doi/10.5555/3305381.3305505

Local causal discovery of direct causes and effects
1. Computing methodologies

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cover image Guide Proceedings

NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2

December 2015

3626 pages

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MIT Press

Cambridge, MA, United States

Publication History

Published: 07 December 2015

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Cited By

Zhang HZhou SYan CGuan JWang X(2019)Recursively learning causal structures using regression-based conditional independence testProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33013108(3108-3115)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33013108
de Carvalho WZarate LHung CPapadopoulos G(2019)Causality relationship among attributes applied in an educational data setProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297406(1271-1277)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297406
Gao TFadnis KCampbell M(2017)Local-to-global Bayesian network structure learningProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305381.3305505(1193-1202)Online publication date: 6-Aug-2017
https://dl.acm.org/doi/10.5555/3305381.3305505

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