Learning Optimal Interventions
Jonas Mueller, David Reshef, George Du, Tommi Jaakkola
Abstract
Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change, we propose a conservative definition of the optimal intervention. Assuming the underlying relationship remains invariant under intervention, we develop efficient algorithms to identify the optimal intervention policy from limited data and provide theoretical guarantees for our approach in a Gaussian Process setting. Although our methods assume covariates can be precisely adjusted, they remain capable of improving outcomes in misspecified settings with unintentional downstream effects. Empirically, our approach identifies good interventions in two practical applications: gene perturbation and writing improvement.
BibTeX
@InProceedings{pmlr-v54-mueller17a,
title = {{Learning Optimal Interventions}},
author = {Mueller, Jonas and Reshef, David and Du, George and Jaakkola, Tommi},
booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
pages = {1039--1047},
year = {2017},
editor = {Singh, Aarti and Zhu, Jerry},
volume = {54},
series = {Proceedings of Machine Learning Research},
month = {20--22 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v54/mueller17a/mueller17a.pdf},
url = {https://proceedings.mlr.press/v54/mueller17a.html},
abstract = {Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change, we propose a conservative definition of the optimal intervention. Assuming the underlying relationship remains invariant under intervention, we develop efficient algorithms to identify the optimal intervention policy from limited data and provide theoretical guarantees for our approach in a Gaussian Process setting. Although our methods assume covariates can be precisely adjusted, they remain capable of improving outcomes in misspecified settings with unintentional downstream effects. Empirically, our approach identifies good interventions in two practical applications: gene perturbation and writing improvement. }
}