Discovering causal structures in binary exclusive-or skew acyclic models

Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara

Research output: Contribution to conferencePaper

1 Citation (Scopus)

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.

Original languageEnglish
Pages373-382
Number of pages10
Publication statusPublished - Sep 29 2011
Externally publishedYes
Event27th Conference on Uncertainty in Artificial Intelligence, UAI 2011 - Barcelona, Spain
Duration: Jul 14 2011Jul 17 2011

Conference

Conference27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
CountrySpain
CityBarcelona
Period7/14/117/17/11

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

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    Inazumi, T., Washio, T., Shimizu, S., Suzuki, J., Yamamoto, A., & Kawahara, Y. (2011). Discovering causal structures in binary exclusive-or skew acyclic models. 373-382. Paper presented at 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, Barcelona, Spain.