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As an important problem in causal inference, we discuss the estimation of treatment effects (TEs).
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Fredrik Johansson, Uri Shalit, and David Sontag · 2016
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Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, and Stefan Bauer · 2019
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Victor Veitch, Yixin Wang, and David Blei · 2019
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Yixin Wang and David M Blei · 2019
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Identifying treatment effects under unobserved confounding by causal representation learning
Anonymous · 2020
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Alexander D’Amour, Peng Ding, Avi Feller, Lihua Lei, and Jasjeet Sekhon · 2020
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Miguel A. Hernan and James M. Robins · 2020
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Bayesian inference of individualized treatment effects using multi-task gaussian processes
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Modern algorithms for matching in observational studies
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An introduction to proximal causal learning
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Changing trends of birth weight with maternal age: a cross-sectional study in xi’an city of northwestern china
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Treatment effect estimation with disentangled latent factors
Weijia Zhang, Lin Liu, and Jiuyong Li · 2020
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