an estimate if SUTVA does not hold. A challenge with this approach is that each subgroup may have substantially less data than the study as a whole, so if the study has been powered to detect the main effects without subgroup analysis, there may not be enough data to properly judge the effects on subgroups. There is some work on detecting heterogenous treatment effects using random forests. Recently, metalearning approaches have been developed that use arbitrary regression frameworks as base learners to infer the CATE. Representation learning can be used to further improve the performance of these methods. References Further reading Estimation theory Medical statistics Experiments