Title: Optimal Targeting in Dynamic Systems Show Chinese title

Title: 动态系统中的最优瞄准

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. Show Chinese abstract

Abstract: 现代针对方法通常依赖于使用机器学习工具估计条件平均处理效应（CATE）。 虽然在识别个体层面受益于治疗的人方面有效，但这些方法通常忽视了当治疗对共享能力产生压力时可能出现的系统级动态。 我们研究了马尔可夫系统中的目标定位问题，其中治疗决策必须在单位到达时逐一做出，早期决策可以通过延迟或有限的资源访问影响后续结果。 我们表明，在这种情况下，最优策略将类似CATE的量与特定状态的阈值进行比较，每个阈值反映了在给定状态下治疗额外个体对系统预期累积影响的反映。 我们提出了一种算法，通过引入政策评估技术来增强标准CATE估计，以从观察数据中估计这些阈值。 理论结果建立了的一致性和收敛保证，实证研究显示，我们的方法相对于个体层面的CATE目标规则显著改善了长期结果。