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• W2904188519 abstract "Classical supervised learning produces unreliable models when training and target distributions differ, with most existing solutions requiring samples from the target domain. We propose a proactive approach which learns a relationship in the training domain that will generalize to the target domain by incorporating prior knowledge of aspects of the data generating process that are expected to differ as expressed in a causal selection diagram. Specifically, we remove variables generated by unstable mechanisms from the joint factorization to yield the Surgery Estimator---an interventional distribution that is invariant to the differences across environments. We prove that the surgery estimator finds stable relationships in strictly more scenarios than previous approaches which only consider conditional relationships, and demonstrate this in simulated experiments. We also evaluate on real world data for which the true causal diagram is unknown, performing competitively against entirely data-driven approaches." @default.
• W2904188519 created "2018-12-22" @default.
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• W2904188519 date "2018-12-11" @default.
• W2904188519 modified "2023-09-28" @default.
• W2904188519 title "Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport." @default.
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• W2904188519 hasPublicationYear "2018" @default.
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