The Recursive Partitioning BLUP (RP-BLUP) for Improved Estimation of Heterogeneous Treatment Effects

Music City Center 

Recent years have seen significant methodological advancements in predicting heterogeneous treatment effects (HTE). However, there is a scarcity of methodological approaches for HTEs arising from random effects. Our work addresses the challenges in estimating HTE stemming from random effects. We particularly focus on developing a methodology for estimating HTE when the design matrix forming the random effects is unidentified, a scenario frequently encountered in many practical fields. When cluster distributions are separated by covariates, we demonstrate that random effects can be estimated through tree nodes. We provide theoretical proofs for consistency. The model is validated Using both simulations and real-world data, compared with causal forests. This approach expands the applicability of tree algorithms and enhances the role of random effects in HTE estimation.

Heterogeneous treatment effect

Random effect

Classification and regression trees

Model misspecification 

Section on Statistical Learning and Data Science