Elias Bareinboim

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We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called ``selection diagrams'' for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures(More)
Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. In this paper, we provide complete graphical and algorithmic conditions for(More)
Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called “transportability”, defined as a license to transfer information learned in experimental studies to a different population, on which only observational studies can be(More)
We review concepts, principles, and tools that unify current approaches to causal analysis, and attend to new challenges presented by big data. In particular, we address the problem of data-fusion – piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) so as to obtain valid(More)
IEEE Intelligent Systems once again selected 10 young AI scientists as " AI's 10 to Watch." This acknowledgment and celebration not only recognizes these young scientists and makes a positive impact in their academic career but also promotes the community and cutting-edge AI research among next-generation AI researchers, the industry, and the(More)