Optimal Targeting in Dynamic Systems

TL;DR

This paper develops a method for optimal treatment targeting in dynamic, resource-constrained systems modeled as Markovian processes, integrating system-level effects into decision-making.

Contribution

It introduces a novel approach that combines CATE estimation with system-level thresholds for dynamic treatment targeting, supported by theoretical guarantees and empirical validation.

Findings

Method improves long-term outcomes compared to traditional CATE targeting.

Threshold-based policies outperform individual-level targeting in dynamic systems.

Theoretical guarantees ensure consistency and convergence of the proposed algorithm.

Abstract

Modern treatment targeting methods often rely on estimating the conditional average treatment effect (CATE) using machine learning tools. While effective in identifying who benefits from treatment on the individual level, these approaches typically overlook system-level dynamics that may arise when treatments induce strain on shared capacity. We study the problem of targeting in Markovian systems, where treatment decisions must be made one at a time as units arrive, and early decisions can impact later outcomes through delayed or limited access to resources. We show that optimal policies in such settings compare CATE-like quantities to state-specific thresholds, where each threshold reflects the expected cumulative impact on the system of treating an additional individual in the given state. We propose an algorithm that augments standard CATE estimation with off-policy evaluation techniques to estimate these thresholds from observational data. Theoretical results establish consistency and convergence guarantees, and empirical studies demonstrate that our method improves long-run outcomes considerably relative to individual-level CATE targeting rules.