Optimal Targeting in Dynamic Systems

2025-06-30

AI Summary

In resource-constrained Markov systems, conventional individualized treatment assignment based on Conditional Average Treatment Effect (CATE) overlooks systemic resource pressure and intertemporal spillovers induced by treatments. Method: We propose a real-time policy optimization framework integrating causal inference with sequential decision-making: (i) extending CATE to a state-dependent dynamic threshold mechanism; (ii) modeling cumulative resource-chain impacts via counterfactual policy evaluation; and (iii) combining off-policy evaluation with observational-data-driven policy learning to ensure estimation consistency while maximizing system-level utility. Results: Empirical evaluation demonstrates that our approach significantly improves long-term population-level outcomes and resource utilization efficiency, outperforming static CATE-based rules. The method ensures scalable, consistent, and policy-aware causal decision-making under resource constraints.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Optimize treatment targeting in dynamic Markovian systems
• Address system-level impacts of treatment capacity strain
• Develop threshold-based policies combining CATE and resource constraints

Innovation

• Uses CATE-like quantities with state-specific thresholds
• Augments CATE estimation with off-policy evaluation
• Optimizes treatment targeting in Markovian dynamic systems