Learning Representations for Counterfactual Inference

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.

Publication
Proceedings of The 33rd International Conference on Machine Learning
Fredrik Johansson
Fredrik Johansson
Postdoc

Assistant Professor, Chalmers University of Technology

Uri Shalit
Uri Shalit
Postdoc

Assistant Professor, Technion

David Sontag
David Sontag
Associate Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

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