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This study promotes a transparent and explicable perspective on data analysis, called eXplainable Data Analysis (XDA). For this reason, we present XInsight, a general framework for XDA. XInsight provides data analysis with qualitative and quantitative explanations of causal and non-causal semantics. This way, it will significantly improve human understanding and confidence in the outcomes of data analysis, facilitating accurate data interpretation and decision making in the real world. XInsight is a three-module, end-to-end pipeline designed to extract causal graphs, translate causal primitives into XDA semantics, and quantify the quantitative contribution of each explanation to a data fact. XInsight uses a set of design concepts and optimizations to address the inherent difficulties associated with integrating causality into XDA. Experiments on synthetic and real-world datasets as well as a user study demonstrate the highly promising capabilities of XInsight.<\/jats:p>","DOI":"10.1145\/3589301","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T20:26:45Z","timestamp":1687292805000},"page":"1-27","source":"Crossref","is-referenced-by-count":11,"title":["XInsight: eXplainable Data Analysis Through The Lens of Causality"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7680-2817","authenticated-orcid":false,"given":"Pingchuan","family":"Ma","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3990-7403","authenticated-orcid":false,"given":"Rui","family":"Ding","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0866-0308","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-6089","authenticated-orcid":false,"given":"Shi","family":"Han","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9230-2799","authenticated-orcid":false,"given":"Dongmei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-020-00633-6"},{"key":"e_1_2_2_2_1","first-page":"4002","volume-title":"International Conference on Artificial Intelligence and Statistics. 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