解决策略性行为下的因果推断问题，提升估计准确性。
Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems
- 融合策略均衡与双重稳健估计，建模策略性干预响应
- 在不同策略强度下降低7.6%至29.3%偏差，表现优于基线方法
- 适合研究策略性个体反应的因果分析，如政策评估、市场实验
我们提出战略双重稳健（SDR）估计器，一种将策略均衡建模与双重稳健估计结合的新框架，用于策略环境中的因果推断。SDR 解决了由策略性行为引发的处理变量内生性问题，在策略性无偏条件下保持双重稳健性。理论分析表明，该方法在策略性无偏假设下具有一致性和渐近正态性。实证评估显示，相比基线方法，SDR 在不同策略强度下实现了7.6%–29.3%的偏差降低，并在大规模代理人场景中保持良好可扩展性。该框架为策略性主体对干预作出反应时提供了可靠的因果推断路径。
原文摘要 · Abstract (English)
We introduce the Strategic Doubly Robust (SDR) estimator, a novel framework that integrates strategic equilibrium modeling with doubly robust estimation for causal inference in strategic environments. SDR addresses endogenous treatment assignment arising from strategic agent behavior, maintaining double robustness while incorporating strategic considerations. Theoretical analysis confirms SDR's consistency and asymptotic normality under strategic unconfoundedness. Empirical evaluations demonstrate SDR's superior performance over baseline methods, achieving 7.6\%-29.3\% bias reduction across varying strategic strengths and maintaining robust scalability with agent populations. The framework provides a principled approach for reliable causal inference when agents respond strategically to interventions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。