Paper

Accuracy Limits of Causal Trees for Individualized Treatment Effects

arXiv:2509.11381v3 Announce Type: replace-cross Abstract: Recursive decision trees are widely used to estimate heterogeneous causal treatment effects in experimental and observational studies. These methods are typically implemented using CART-type recursive partitioning, with splitting criteria designed to identify variation in treatment effects across covariate-defined subgroups. We study causal tree estimators based on adaptive recursive partitioning and establish lower bounds on their estimation accuracy. The class we analyze includes versions with and without sample splitting, based on c…

Authors:

Related discussions