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In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
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Extensions of lipschitz mappings into a hilbert space
William B Johnson and Joram Lindenstrauss · 1984
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Applied logistic regression analysis , volume 106
Scott Menard · 2002
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Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Jared K Lunceford and Marie Davidian · 2004
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Correcting sample selection bias in maximum entropy density estimation
Miroslav Dudík, Steven J Phillips, and Robert E Schapire · 2006
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Greedy layer-wise training of deep networks
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe · 2008
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Discriminative learning under covariate shift
Steffen Bickel, Michael Brückner, and Tobias Scheffer · 2009
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An introduction to propensity score methods for reducing the effects of confounding in observational studies
Peter C Austin · 2011
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Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies
Jens Hainmueller · 2012
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Yaoliang Yu and Csaba Szepesvári · 2012
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Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Robust learning under uncertain test distributions: Relating covariate shift to model misspecification
Junfeng Wen, Chun-Nam Yu, and Russell Greiner · 2014
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Causal transfer in machine learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2015
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Stable weights that balance covariates for estimation with incomplete outcome data
José R Zubizarreta · 2015
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Approximate residual balancing: De-biased inference of average treatment effects in high dimensions
Susan Athey, Guido W Imbens, and Stefan Wager · 2016
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Steering social media promotions with effective strategies
Kun Kuang, Meng Jiang, Peng Cui, and Shiqiang Yang · 2016
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Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Estimating treatment effect in the wild via differentiated confounder balancing
Kun Kuang, Peng Cui, Bo Li, Meng Jiang, and Shiqiang Yang
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Treatment effect estimation with data-driven variable decomposition
Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Shiqiang Yang, and Fei Wang
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Effective promotional strategies selection in social media: A data-driven approach
Kun Kuang, Meng Jiang, Peng Cui, Jiashen Sun, and Shiqiang Yang
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Matching on balanced nonlinear representations for treatment effects estimation
Sheng Li and Yun Fu · 2017
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Adaptive dropout with rademacher complexity regularization
Ke Zhai and Huan Wang · 2018
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