Discovering causal structures in binary exclusive-or skew acyclic models

Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:421-430, 2011.

Abstract

Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discover an identifiable causal structure have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limited to continuous data. In this paper, we present a novel causal model for binary data and propose a new approach to derive an identifiable causal structure governing the data based on skew Bernoulli distributions of external noise. Experimental evaluation shows excellent performance for both artificial and real world data sets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-inazumi11a, title = {Discovering causal structures in binary exclusive-or skew acyclic models}, author = {Inazumi, Takanori and Washio, Takashi and Shimizu, Shohei and Suzuki, Joe and Yamamoto, Akihiro and Kawahara, Yoshinobu}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {421--430}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/inazumi11a/inazumi11a.pdf}, url = {https://proceedings.mlr.press/r9/inazumi11a.html}, abstract = {Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discover an identifiable causal structure have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limited to continuous data. In this paper, we present a novel causal model for binary data and propose a new approach to derive an identifiable causal structure governing the data based on skew Bernoulli distributions of external noise. Experimental evaluation shows excellent performance for both artificial and real world data sets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Discovering causal structures in binary exclusive-or skew acyclic models %A Takanori Inazumi %A Takashi Washio %A Shohei Shimizu %A Joe Suzuki %A Akihiro Yamamoto %A Yoshinobu Kawahara %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-inazumi11a %I PMLR %P 421--430 %U https://proceedings.mlr.press/r9/inazumi11a.html %V R9 %X Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discover an identifiable causal structure have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limited to continuous data. In this paper, we present a novel causal model for binary data and propose a new approach to derive an identifiable causal structure governing the data based on skew Bernoulli distributions of external noise. Experimental evaluation shows excellent performance for both artificial and real world data sets. %Z Reissued by PMLR on 04 October 2026.
APA
Inazumi, T., Washio, T., Shimizu, S., Suzuki, J., Yamamoto, A. & Kawahara, Y.. (2011). Discovering causal structures in binary exclusive-or skew acyclic models. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:421-430 Available from https://proceedings.mlr.press/r9/inazumi11a.html. Reissued by PMLR on 04 October 2026.

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