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Amount of causal feature selection algorithms have achieved a great deal of success in classification and prediction tasks. However, the descriptive information of data is collected from different data sources in many practical applications. While few researches focus on the causal variable discovery in multisource environments due to the complex causal relationships. To address these problems, we propose a causal feature selection framework in multisource environments to solve the above problems. Firstly, we mine the causal mechanism with respect to the class attribute under the assumption that only a single data source is included. Secondly, by utilizing the concept of causal invariance in causal inference, we formulate the problem of causal feature selection with multiple data sources as a search problem for an invariant set across data sources. In addition, we give the upper and lower bounds of the causal invariant set. Finally, we design a novel multisource multilabel causal feature selection (MMCFS) algorithm. To verify the effectiveness of the proposed algorithm, we compare it with 12 feature selection methods on synthetic datasets. Experiment results show that the classification performance of MMCFS achieves highly competitive performance against other comparing algorithms.<\/jats:p>","DOI":"10.1002\/cpe.7067","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T16:11:40Z","timestamp":1652717500000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multilabel causal variable discovery in multisource"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4063-5847","authenticated-orcid":false,"given":"Yun\u2010an","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science Minnan Normal University  Zhangzhou China"},{"name":"Lab of Data Science and Intelligence Application Minnan Normal University  Zhangzhou China"}]},{"given":"Yaojin","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science Minnan Normal University  Zhangzhou China"},{"name":"Lab of Data Science and Intelligence Application Minnan Normal University  Zhangzhou China"}]},{"given":"Xiehua","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer and Information MinNan Science and Technology University  Quanzhou China"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science Minnan Normal University  Zhangzhou China"},{"name":"Lab of Data Science and Intelligence Application Minnan Normal University  Zhangzhou China"}]},{"given":"Zhisen","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science Minnan Normal University  Zhangzhou China"},{"name":"Lab of Data Science and Intelligence Application Minnan Normal University  Zhangzhou China"}]},{"given":"Jianfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Evecom Technology Co., Ltd.  Fujian China"}]},{"given":"Shaozi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Xiamen University  Xiamen China"}]}],"member":"311","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"171","article-title":"Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation","volume":"11","author":"Aliferis C","year":"2010","journal-title":"J Mach Learn Res"},{"key":"e_1_2_8_3_1","first-page":"235","article-title":"Local causal and Markov blanket induction for causal discovery and feature selection for classification part II: analysis and extensions","volume":"11","author":"Aliferis C","year":"2010","journal-title":"J Mach Learn Res"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.08.009"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007649029923"},{"key":"e_1_2_8_7_1","unstructured":"TrohidisK TsoumakasG KallirisG VlahavasI.Multi\u2010label classification of music into emotions. 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