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First, we present a causal discovery method for traditional additive noise models that identifies the causal direction by analyzing the supports of the conditional distributions. Then, we present a causal mixture model to address the problem that the function transforming cause to effect varies across the observations. We propose a novel method called Support Analysis (SA) for causal discovery with the mixture model. Experiments using synthetic and real data are presented to demonstrate the performance of our proposed algorithm.<\/jats:p>","DOI":"10.1145\/2700477","type":"journal-article","created":{"date-parts":[[2015,12,4]],"date-time":"2015-12-04T13:43:07Z","timestamp":1449236587000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Causal Discovery on Discrete Data with Extensions to Mixture Model"],"prefix":"10.1145","volume":"7","author":[{"given":"Furui","family":"Liu","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, N.T. Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laiwan","family":"Chan","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, N.T. 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