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The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed.
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Zhiqiang Tan · 2006
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Masashi Sugiyama, Taiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul Von Bünau, and Motoaki Kawanabe · 2008
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An ultra-brief screening scale for anxiety and depression: the phq–4
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Minimax regret treatment choice with finite samples
Jörg Stoye · 2009
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Subsidizing vocational training for disadvantaged youth in colombia: Evidence from a randomized trial
Orazio Attanasio, Adriana Kugler, and Costas Meghir · 2011
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The use of propensity scores to assess the generalizability of results from randomized trials
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Estimating individualized treatment rules using outcome weighted learning
Yingqi Zhao, Donglin Zeng, A John Rush, and Michael R Kosorok · 2012
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Interval estimation of population means under unknown but bounded probabilities of sample selection
Peter M Aronow and Donald KK Lee · 2013
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Improving generalizations from experiments using propensity score subclassification: Assumptions, properties, and contexts
Elizabeth Tipton · 2013
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A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients
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Unrepresentative big surveys significantly overestimated us vaccine uptake
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Shape-constrained partial identification of a population mean under unknown probabilities of sample selection
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Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations
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Behavioral risk factor surveillance system survey data, 2021
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External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients
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