Machine learning for subgroup discovery under treatment effect
Methodology
2019-02-28 v1 Machine Learning
Machine Learning
Abstract
In many practical tasks it is needed to estimate an effect of treatment on individual level. For example, in medicine it is essential to determine the patients that would benefit from a certain medicament. In marketing, knowing the persons that are likely to buy a new product would reduce the amount of spam. In this chapter, we review the methods to estimate an individual treatment effect from a randomized trial, i.e., an experiment when a part of individuals receives a new treatment, while the others do not. Finally, it is shown that new efficient methods are needed in this domain.
Keywords
Cite
@article{arxiv.1902.10327,
title = {Machine learning for subgroup discovery under treatment effect},
author = {Aleksey Buzmakov},
journal= {arXiv preprint arXiv:1902.10327},
year = {2019}
}
Comments
32 pages, in Russian, 1 figure, 6 tables