通过局部解释揭示策略行为前的原始特征，提升单次决策评估准确性。
Off-Policy Evaluation with Strategic Agents via Local Disclosure
- 利用事后解释披露局部信息，还原策略调整前的原始协变量。
- 在仅知部分响应行为下，提出双重稳健估计器并证明其一致性。
- 适用于决策者对策略行为了解有限的现实场景，如医疗或信贷评估。
我们研究策略行为下的离策略评估（OPE），其中决策对象（或代理）会根据决策者的策略主动调整自身协变量。这种行为导致政策依赖的协变量偏移，破坏了现有方法中协变量与政策无关的标准假设。以往工作通过强假设（如重复交互或完全掌握代理响应行为）应对该挑战，严重限制了实际应用。本文考虑单次离策略评估设置，决策者仅部分知晓代理响应行为。关键洞察是：通过事后解释披露局部信息，可揭示代理在策略适应前的原始协变量，缓解策略行为带来的信息损失。基于此结构，我们构建了代理响应的统计模型，并设计一个双重稳健的策略价值估计器。假设代理的成本敏感度服从条件对数正态分布，我们证明了估计器的一致性，并通过实证验证了方法有效性。更广泛地，结果表明交互设计可通过揭示代理策略响应中的隐藏结构，缓解信息不对称问题。
原文摘要 · Abstract (English)
We study off-policy evaluation (OPE) under strategic behavior where decision subjects (or agents) respond to a decision maker's policy by strategically modifying their covariates. Such behavior induces a policy-dependent covariate shift, breaking the standard assumption in existing methods that covariates are exogenous to the policy. Related work addresses this challenge by imposing strong assumptions such as repeated interactions or full knowledge of agents' response behavior, substantially limiting its applicability to OPE. In contrast, we consider a one-shot OPE setting where the decision maker has only partial knowledge of the agents' response behavior. Our key insight is that disclosing local information through post-hoc explanations reveals agents' pre-strategic covariates prior to adaptation, mitigating the information loss induced by strategic behavior. Leveraging this structure, we estimate a statistical model for the agents' responses and construct a doubly robust estimator for policy value. By assuming that the agents' cost sensitivity follows a conditional log-normal distribution, we establish consistency of the proposed estimator and validate our approach empirically. More broadly, our results highlight how interaction design can mitigate information asymmetry by revealing otherwise hidden structure in agents' strategic responses.
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