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Analysts often only have access to partial data, and they critically rely on (often unavailable or incomplete) domain knowledge to identify attributes to include for analysis, which is often given in the form of a causal DAG. We argue that data management techniques can surmount both of these challenges. In this work, we introduce the Causal Data Integration (CDI) problem, in which unobserved attributes are mined from external sources and a corresponding causal DAG is automatically built. We identify key challenges and research opportunities in designing a CDI system, and present a system architecture for solving the CDI problem. Our preliminary experimental results demonstrate that solving CDI is achievable and pave the way for future research.<\/jats:p>","DOI":"10.14778\/3603581.3603602","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T19:06:48Z","timestamp":1691521608000},"page":"2659-2665","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Causal Data Integration"],"prefix":"10.14778","volume":"16","author":[{"given":"Brit","family":"Youngmann","sequence":"first","affiliation":[{"name":"CSAIL MIT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Cafarella","sequence":"additional","affiliation":[{"name":"CSAIL, MIT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Babak","family":"Salimi","sequence":"additional","affiliation":[{"name":"University of California, San Diego"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Zeng","sequence":"additional","affiliation":[{"name":"CSAIL, MIT"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2020. 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