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We consider the task of counterfactual estimation from observational imaging data given a known causal structure.
Gradient-based learning applied to document recognition
Y Lecun, L Bottou, Y Bengio, and P Haffner · 1998
Earlier work this paper cites.
An overview of statistical learning theory
Vladimir N Vapnik · 1999
Earlier work this paper cites.
Stochastic Differential Equations: An Introduction with Applications
Bernt Øksendal · 2003
Earlier work this paper cites.
Estimation of Non-Normalized Statistical Models by Score Matching
Aapo Hyvärinen · 2005
Earlier work this paper cites.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Causality
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Earlier work this paper cites.
Bayesian Nonparametric Modeling for Causal Inference
Jennifer Hill · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
From Ordinary Differential Equations to Structural Causal Models: The Deterministic Case
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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O Ronneberger, P.Fischer, and T Brox · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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