{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:29:11Z","timestamp":1750220951804,"version":"3.41.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:00:00Z","timestamp":1568246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"973 Program of China","award":["2015CB856000"],"award-info":[{"award-number":["2015CB856000"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["11331011, 11771028, 91630314"],"award-info":[{"award-number":["11331011, 11771028, 91630314"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2019,9,30]]},"abstract":"<jats:p>Causal networks are used to describe and to discover causal relationships among variables and data generating mechanisms. There have been many approaches for learning a global causal network of all observed variables. In many applications, we may be interested in finding what are the effects of a specified cause variable and what are the causal paths from the cause variable to its effects. Instead of learning a global causal network, we propose several local learning approaches for finding all effects (or descendants) of the specified cause variable and the causal paths from the cause variable to some effect variable of interest. We discuss the identifiability of the effects and the causal paths from observed data and prior knowledge. For the case that the causal paths are not identifiable, our approaches try to find a path set that contains the causal paths of interest.<\/jats:p>","DOI":"10.1145\/3313147","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T12:28:56Z","timestamp":1568377736000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths"],"prefix":"10.1145","volume":"10","author":[{"given":"Yue","family":"Liu","sequence":"first","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Zheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Tencent Technology (Shenzhen) Co. Ltd, Shenzhen, China"}]},{"given":"Chunchen","family":"Liu","sequence":"additional","affiliation":[{"name":"NEC Laboratories, Beijing, China"}]},{"given":"Zhi","family":"Geng","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1031833662"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244302760200696"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-060116-053803"},{"key":"e_1_2_1_4_1","article-title":"Evaluation of causal effects and local structure learning of causal networks","volume":"6","author":"Geng Zhi","year":"2019","unstructured":"Zhi Geng , Yue Liu , Chunchen Liu , and Wang Miao . 2019 . Evaluation of causal effects and local structure learning of causal networks . Ann. Rev. Stat. Appl. 6 , 1 (2019). Zhi Geng, Yue Liu, Chunchen Liu, and Wang Miao. 2019. Evaluation of causal effects and local structure learning of causal networks. Ann. Rev. Stat. Appl. 6, 1 (2019).","journal-title":"Ann. Rev. Stat. Appl."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00340"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS685"},{"key":"e_1_2_1_7_1","volume-title":"Causality: Models, Reasoning, and Inference","author":"Pearl Judea","year":"2000","unstructured":"Judea Pearl . 2000 . Causality: Models, Reasoning, and Inference . Cambridge University Press , Cambridge, UK . Judea Pearl. 2000. Causality: Models, Reasoning, and Inference. 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In Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 (Proceedings of Machine Learning Research) , Vol. 3 . PMLR, 93--105. Jianxin Yin, You Zhou, Changzhang Wang, Ping He, Cheng Zheng, and Zhi Geng. 2008. Partial orientation and local structural learning of causal networks for prediction. In Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 (Proceedings of Machine Learning Research), Vol. 3. PMLR, 93--105."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3313147","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3313147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:53:59Z","timestamp":1750204439000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3313147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,12]]},"references-count":13,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,9,30]]}},"alternative-id":["10.1145\/3313147"],"URL":"https:\/\/doi.org\/10.1145\/3313147","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"type":"print","value":"2157-6904"},{"type":"electronic","value":"2157-6912"}],"subject":[],"published":{"date-parts":[[2019,9,12]]},"assertion":[{"value":"2018-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-09-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}