In settings where units' outcomes are affected by others' treatments, there has been a proliferation of ways to quantify effects of treatments on outcomes, including via indirect exposure to other units' treatments. Here we consider two properties we might want estimands to have: being interpretable as summaries of unit-level effects, and being relevant to choice of a policy governing treatment assignment. We characterize many estimands as involving one of two orders of averaging over units in a population and over treatment assignments under a policy. The more common representation often results in quantities that are insufficient for optimal policy choice. This occurs because these quantities summarize outcomes under homogeneous exposure to treatment, but even homogeneous policies often lead to heterogeneous exposures. The other representation often yields quantities that lack an interpretation as summaries of unit-level effects. We argue that, among various estimands, the expected average outcome, which averages over units and treatment assignments in either order, deserves further attention from researchers. This estimand, or contrasts among these estimands under different policies, is both a summary of unit-level effects and is sufficient for optimal policy choice with utilitarian welfare.

翻译：在个体结果受他人处理影响的场景中，量化处理对结果影响的方式（包括通过其他个体处理的间接暴露）已大量涌现。本文探讨了估计量可能应具备的两个属性：可解释为个体层面效应的汇总，以及与处理分配政策选择的相关性。我们将许多估计量描述为涉及对总体中个体和特定政策下处理分配的两种不同顺序的求平均。更常见的表示方式通常导致所得量不足以支持最优政策选择。这是因为这些量汇总了在均匀处理暴露下的结果，但即使均匀政策也常导致非均匀的暴露。另一种表示方式则往往产生缺乏作为个体层面效应汇总解释的量。我们认为，在各种估计量中，期望平均结果（无论以何种顺序对个体和处理分配求平均）值得研究者进一步关注。该估计量，或不同政策下这些估计量间的对比，既是个体层面效应的汇总，也足以支持基于功利主义福利的最优政策选择。