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The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks.<\/jats:p>","DOI":"10.1073\/pnas.1510507113","type":"journal-article","created":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T18:11:32Z","timestamp":1467742292000},"page":"7345-7352","update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":418,"title":["Causal inference and the data-fusion problem"],"prefix":"10.1073","volume":"113","author":[{"given":"Elias","family":"Bareinboim","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of California, Los Angeles, CA 90095;"},{"name":"Department of Computer Science, Purdue University, West Lafayette, IN 47907"}]},{"given":"Judea","family":"Pearl","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, Los Angeles, CA 90095;"}]}],"member":"341","published-online":{"date-parts":[[2016,7,5]]},"reference":[{"key":"e_1_3_2_1_2","doi-asserted-by":"crossref","unstructured":"J Pearl Causality: Models Reasoning and Inference (Cambridge Univ Press New York) 2nd Ed. 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