ICML 2016poster970 citations
Learning Representations for Counterfactual Inference
Fredrik Johansson, Uri Shalit, David Sontag
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.
BibTeX
@InProceedings{pmlr-v48-johansson16,
title = {Learning Representations for Counterfactual Inference},
author = {Johansson, Fredrik and Shalit, Uri and Sontag, David},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {3020--3029},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/johansson16.pdf},
url = {https://proceedings.mlr.press/v48/johansson16.html},
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.}
}