Applied work with interference typically models outcomes as functions of own treatment and a low-dimensional exposure mapping of others' treatments, even when that mapping may be misspecified. This raises a basic question: what policy object are exposure-based estimands implicitly targeting, and how should we interpret their direct and spillover components relative to the underlying policy question? We take as primitive the marginal policy effect, defined as the effect of a small change in the treatment probability under the actual experimental design, and show that any researcher-chosen exposure mapping induces a unique pseudo-true outcome model. This model is the best approximation to the underlying potential outcomes that depends only on the user-chosen exposure. Utilizing that representation, the marginal policy effect admits a canonical decomposition into exposure-based direct and spillover effects, and each component provides its optimal approximation to the corresponding oracle objects that would be available if interference were fully known. We then focus on a setting that nests important empirical and theoretical applications in which both local network spillovers and global spillovers, such as market equilibrium, operate. There, the marginal policy effect further decomposes asymptotically into direct, local, and global channels. An important implication is that many existing methods are more robust than previously understood once we reinterpret their targets as channel-specific components of this pseudo-true policy estimand. Simulations and a semi-synthetic experiment calibrated to a large cash-transfer experiment show that these components can be recovered in realistic experimental designs.

翻译：在存在干扰的应用研究中，即使暴露映射可能存在误设，通常仍将结果建模为自身处理与他人处理的低维暴露映射的函数。这引发了一个基本问题：基于暴露的估计量隐式地指向何种政策目标？我们应如何根据潜在政策问题解释其直接效应与溢出效应分量？我们将边际政策效应作为基本设定，该效应定义为在实际实验设计下处理概率微小变化所产生的影响，并证明任何研究者选择的暴露映射都会诱导出唯一的伪真实结果模型。该模型是仅依赖于用户所选暴露的潜在结果的最佳近似。利用该表示，边际政策效应可规范分解为基于暴露的直接效应与溢出效应，每个分量都为其对应理想对象（若干扰完全已知时可得）提供了最优近似。随后，我们聚焦于一个嵌套重要实证与理论应用的情境，其中同时存在局部网络溢出（如同伴效应）与全局溢出（如市场均衡）机制。在此情境下，边际政策效应可渐近分解为直接效应、局部通道与全局通道。一个重要启示是：一旦我们将现有方法的目标重新解释为此伪真实政策估计量的通道特定分量，就会发现它们比既往认知更具稳健性。基于大型现金转移实验校准的模拟与半合成实验表明，这些分量可在现实实验设计中得到有效估计。