Estimating heterogeneous treatment effects versus building individualized treatment rules: Connection and disconnection

TL;DR

This paper explores the relationship between estimating heterogeneous treatment effects and developing individualized treatment rules, revealing that better effect estimation does not always improve treatment decisions, with theoretical insights and simulation evidence.

Contribution

It provides a theoretical framework clarifying when improved treatment effect estimation impacts treatment rules and when it does not, highlighting practical implications.

Findings

Better effect estimation may not improve treatment decisions.

Theoretical analysis of connection and disconnection scenarios.

Simulation studies illustrating key points.

Abstract

Estimating heterogeneous treatment effects is a well-studied topic in the statistics literature. More recently, it has regained attention due to an increasing need for precision medicine as well as the increased use of state-of-art machine learning methods in the estimation. Furthermore, estimating heterogeneous treatment effects is directly related to building an individualized treatment rule, which is a decision rule of treatment according to patient characteristics. This paper examines the connection and disconnection between these two research problems. Notably, a better estimation of the heterogeneous treatment effects may or may not lead to a better individualized treatment rule. We provide theoretical frameworks to explain the connection and disconnection and demonstrate two different scenarios through simulations. Our conclusion sheds light on a practical guide that under certain circumstances, there is no need to enhance estimation of the treatment effects, as it does not alter the treatment decision.