Using Recursive Partitioning to Find and Estimate Heterogenous Treatment Effects In Randomized Clinical Trials

TL;DR

This paper develops a recursive partitioning method to identify and estimate heterogeneous treatment effects in randomized clinical trials, addressing challenges in post-selection inference and local effect estimation.

Contribution

It introduces a novel recursive partitioning approach tailored for analyzing heterogenous effects in clinical trial data, with solutions for statistical inference after data-driven subgroup discovery.

Findings

Effective identification of subgroups with different treatment responses

Improved estimation of local average treatment effects

Addressed statistical inference challenges post subgroup selection

Abstract

Heterogeneous treatment effects can be very important in the analysis of randomized clinical trials. Heightened risks or enhanced benefits may exist for particular subsets of study subjects. When the heterogeneous treatment effects are specified as the research is being designed, there are proper and readily available analysis techniques. When the heterogeneous treatment effects are inductively obtained as an experiment's data are analyzed, significant complications are introduced. There can be a need for special loss functions designed to find local average treatment effects and for techniques that properly address post selection statistical inference. In this paper, we tackle both while undertaking a recursive partitioning analysis of a randomized clinical trial testing whether individuals on probation, who are low risk, can be minimally supervised with no increase in recidivism.