Machine learning for subgroup discovery under treatment effect
This is an incremental review chapter addressing the problem of subgroup discovery for treatment effect estimation in fields like medicine and marketing.
The paper reviews methods for estimating individual treatment effects from randomized trials, highlighting the need for new efficient approaches in this domain.
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.