来自竞争性处理的因果推断
Causal Inference from Competing Treatments
AI总结：
本文研究竞争性处理下的因果推断问题，构建了基于竞价系统和最小化估计误差效用函数的博弈论模型，证明了易处理目标存在纯纳什均衡且为一般目标的近似均衡，揭示了竞争环境下实验的均衡行为。
摘要：
RCT的许多应用涉及多个处理管理员的存在——从实地实验到在线广告——他们竞争受试者的注意力。在竞争面前，估计因果效应变得困难，因为受试者看到处理的位置会影响他们的反应，从而影响处理效应。在本文中，我们构建了一个博弈论模型，涉及希望在竞争存在的情况下估计因果效应的智能体，通过一个竞价系统和一个最小化估计误差的效用函数来实现。我们的主要技术结果建立了一个具有易处理目标的近似，该目标最大化了通过在受试者上战略性地分配预算所获得的样本价值。这使我们能够在模型中找到均衡：我们表明易处理目标具有纯纳什均衡，并且在广泛条件下，任何纳什均衡都是最小化估计误差的一般目标的近似均衡。从概念上讲，我们的工作成功结合了因果推断和博弈论的元素，以阐明竞争下实验的均衡行为。
英文摘要：
Many applications of RCTs involve the presence of multiple treatment administrators -- from field experiments to online advertising -- that compete for the subjects' attention. In the face of competition, estimating a causal effect becomes difficult, as the position at which a subject sees a treatment influences their response, and thus the treatment effect. In this paper, we build a game-theoretic model of agents who wish to estimate causal effects in the presence of competition, through a bidding system and a utility function that minimizes estimation error. Our main technical result establishes an approximation with a tractable objective that maximizes the sample value obtained through strategically allocating budget on subjects. This allows us to find an equilibrium in our model: we show that the tractable objective has a pure Nash equilibrium, and that any Nash equilibrium is an approximate equilibrium for our general objective that minimizes estimation error under broad conditions. Conceptually, our work successfully combines elements from causal inference and game theory to shed light on the equilibrium behavior of experimentation under competition.
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