标题： 通过时间链接、观察性和实验性数据识别长期治疗效果 显示英文标题

标题： Identification of Long-Term Treatment Effects via Temporal Links, Observational, and Experimental Data

摘要： 近期文献提出结合短期实验数据和长期观察数据，以提供可信的替代方案，取代传统的观察研究，用于识别长期平均治疗效应（LTEs）。 我表明，在这种情况下，实验数据起到了辅助作用。 如果没有额外的建模假设，实验数据不具备识别能力。 当施加建模假设时，实验数据有助于增强其识别能力。 如果假设不成立，增加实验数据可能只会得到更偏离真实情况的结果。 受此启发，我提出了两个关于治疗反应的假设，这些假设可能基于经济理论或直觉具有合理性。 为了利用这些假设，我开发了一种新的两步识别方法，该方法围绕着时间连接函数的边界展开——即短期与长期平均潜在结果之间的关系。 该方法为一般类别的假设提供了LTEs的尖锐边界，并允许实验合规性存在缺陷——扩展了现有结果。 显示英文摘要

摘要： Recent literature proposes combining short-term experimental and long-term observational data to provide credible alternatives to conventional observational studies for identification of long-term average treatment effects (LTEs). I show that experimental data have an auxiliary role in this context. They bring no identifying power without additional modeling assumptions. When modeling assumptions are imposed, experimental data serve to amplify their identifying power. If the assumptions fail, adding experimental data may only yield results that are farther from the truth. Motivated by this, I introduce two assumptions on treatment response that may be defensible based on economic theory or intuition. To utilize them, I develop a novel two-step identification approach that centers on bounding temporal link functions -- the relationship between short-term and mean long-term potential outcomes. The approach provides sharp bounds on LTEs for a general class of assumptions, and allows for imperfect experimental compliance -- extending existing results.