Machine learning for subgroup discovery under treatment effect

TL;DR

This paper reviews methods for estimating individual treatment effects in randomized trials, highlighting the need for more efficient approaches to identify who benefits from treatments in fields like medicine and marketing.

Contribution

It provides a comprehensive review of existing methods for subgroup discovery under treatment effects and emphasizes the necessity for developing new, more efficient techniques.

Findings

Existing methods are limited in efficiency

New methods are needed for better subgroup identification

The paper highlights gaps in current approaches

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.