随机潜在选择和结果
Stochastic Potential Choices and Outcomes
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中文总结 AI 辅助
本文探讨个体层面结果和处理分配或选择为随机时的变化，提出含处理选择概率和潜在结果分布的模型，改变常见估计量解释，还为随机选择给出基于信息的基础。
中文摘要 AI 辅助
应用计量经济学家通常将每个个体建模为在处理和控制下有固定结果，在工具变量（IV）设置中，在工具的每个值下有固定的处理决策。本文探讨当结果和处理分配或选择在个体层面是随机时会有什么变化。在模型中，每个个体有一个稳定（但可能是随机）的响应类型，由两个对象组成：每个状态下的处理选择概率和每个处理 - 状态对下的潜在结果分布。这些随机潜在结果改变了一些常见估计量的解释。例如，在确定性IV模型中，估计量仅识别顺从者的处理效果——那些处理状态随工具而改变的人。在随机处理分配或选择下，不存在这样的子群体：估计量对所有个体的效果进行平均，根据工具、政策或分配规则改变其处理概率的程度对每个个体进行加权。然后本文为随机选择给出了一个基于信息的基础，其中个体根据给定信息的预期收益采取行动。
英文摘要
Applied econometricians typically model each individual as having fixed outcomes under treatment and control and, in instrumental-variables (IV) settings, fixed treatment decisions under each value of the instrument. This paper asks what changes when outcomes and treatment allocations or choices are stochastic at the individual level. In the model, each individual has a stable (but possibly stochastic) response type consisting of two objects: a treatment choice probability under each state and a potential outcome distribution under each treatment-state pair. These stochastic potential outcomes change the interpretation of some familiar estimators. For instance, in the deterministic IV model, the estimand identifies treatment effect only for compliers-those whose treatment status switches with the instrument. Under stochastic treatment allocation or choice there is no such discrete subgroup: the estimand averages effects over the population, weighing each individual by how much the instrument, policy, or assignment rule moves their probability of treatment. The paper then gives an information-based foundation for stochastic choice, in which individuals act on expected gains given their information. Finally, repeated choices give the stable-kernel formulation empirical content: short panels identify moments of individual treatment probabilities, while long panels identify the joint dependence between treatment effects and the probability movements induced by an instrument or policy.
