Learning representations for counterfactual inference

Fredrik D. Johansson, Uri Shalit, David Sontag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication33rd International Conference on Machine Learning, ICML 2016
PublisherInternational Machine Learning Society (IMLS)
Pages4407-4418
Number of pages12
Volume6
ISBN (Electronic)9781510829008
StatePublished - 2016
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: Jun 19 2016Jun 24 2016

Other

Other33rd International Conference on Machine Learning, ICML 2016
CountryUnited States
CityNew York City
Period6/19/166/24/16

Fingerprint

Ecology
Sugars
Learning algorithms
Blood
Education
Deep learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications

Cite this

Johansson, F. D., Shalit, U., & Sontag, D. (2016). Learning representations for counterfactual inference. In 33rd International Conference on Machine Learning, ICML 2016 (Vol. 6, pp. 4407-4418). International Machine Learning Society (IMLS).

Learning representations for counterfactual inference. / Johansson, Fredrik D.; Shalit, Uri; Sontag, David.

33rd International Conference on Machine Learning, ICML 2016. Vol. 6 International Machine Learning Society (IMLS), 2016. p. 4407-4418.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Johansson, FD, Shalit, U & Sontag, D 2016, Learning representations for counterfactual inference. in 33rd International Conference on Machine Learning, ICML 2016. vol. 6, International Machine Learning Society (IMLS), pp. 4407-4418, 33rd International Conference on Machine Learning, ICML 2016, New York City, United States, 6/19/16.
Johansson FD, Shalit U, Sontag D. Learning representations for counterfactual inference. In 33rd International Conference on Machine Learning, ICML 2016. Vol. 6. International Machine Learning Society (IMLS). 2016. p. 4407-4418
Johansson, Fredrik D. ; Shalit, Uri ; Sontag, David. / Learning representations for counterfactual inference. 33rd International Conference on Machine Learning, ICML 2016. Vol. 6 International Machine Learning Society (IMLS), 2016. pp. 4407-4418
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