Deep IV: A Flexible Approach for Counterfactual Prediction
Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy
Abstract
Counterfactual prediction requires understanding causal relationships between so-called treatment and outcome variables. This paper provides a recipe for augmenting deep learning methods to accurately characterize such relationships in the presence of instrument variables (IVs) – sources of treatment randomization that are conditionally independent from the outcomes. Our IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework allows us to take advantage of off-the-shelf supervised learning techniques to estimate causal effects by adapting the loss function. Experiments show that it outperforms existing machine learning approaches.
BibTeX
@InProceedings{pmlr-v70-hartford17a,
title = {Deep {IV}: A Flexible Approach for Counterfactual Prediction},
author = {Jason Hartford and Greg Lewis and Kevin Leyton-Brown and Matt Taddy},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {1414--1423},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf},
url = {https://proceedings.mlr.press/v70/hartford17a.html},
abstract = {Counterfactual prediction requires understanding causal relationships between so-called treatment and outcome variables. This paper provides a recipe for augmenting deep learning methods to accurately characterize such relationships in the presence of instrument variables (IVs) – sources of treatment randomization that are conditionally independent from the outcomes. Our IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework allows us to take advantage of off-the-shelf supervised learning techniques to estimate causal effects by adapting the loss function. Experiments show that it outperforms existing machine learning approaches.}
}