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.
Cite
Text
Johansson et al. "Learning Representations for Counterfactual Inference." International Conference on Machine Learning, 2016.Markdown
[Johansson et al. "Learning Representations for Counterfactual Inference." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/johansson2016icml-learning/)BibTeX
@inproceedings{johansson2016icml-learning,
title = {{Learning Representations for Counterfactual Inference}},
author = {Johansson, Fredrik and Shalit, Uri and Sontag, David},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {3020-3029},
volume = {48},
url = {https://mlanthology.org/icml/2016/johansson2016icml-learning/}
}