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Learning representations for counterfactual inference

Fredrik Johansson (Institutionen för data- och informationsteknik, Datavetenskap (Chalmers)) ; U. Shalit ; D. Sontag
33rd International Conference on Machine Learning, ICML 2016, New York City, United States; 19 June 2016 through 24 June 2016 p. 4407-4418. (2016)
[Konferensbidrag, refereegranskat]

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art. © 2016 by the author(s).

Nyckelord: Artificial intelligence, Learning systems, Algorithmic framework, Blood sugars, Causal inferences, Domain adaptation, Empirical - comparisons, Observational data, Observational study, State of the art, Learning algorithms

Denna post skapades 2017-01-19.
CPL Pubid: 247399