{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:24:55Z","timestamp":1705019095603},"reference-count":25,"publisher":"MIT Press - Journals","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:p> Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding\u2013based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods. <\/jats:p>","DOI":"10.1162\/neco_a_01064","type":"journal-article","created":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T21:36:27Z","timestamp":1517348187000},"page":"1394-1425","source":"Crossref","is-referenced-by-count":2,"title":["A Kernel Embedding\u2013Based Approach for Nonstationary Causal Model Inference"],"prefix":"10.1162","volume":"30","author":[{"given":"Shoubo","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhitang","family":"Chen","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei, Hong Kong Science Park, Hong Kong 999077"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laiwan","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"B1","first-page":"1027","volume-title":"Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms","author":"Arthur D.","year":"2007"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00599"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(02)00191-1"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(94)90029-9"},{"key":"B5","volume-title":"Socioeconomic background and achievement","author":"Duncan O. 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