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.

翻译：近期文献提出结合短期实验数据与长期观测数据，为识别长期平均处理效应提供比传统观测研究更可靠的替代方案。本文指出，在此类研究中实验数据仅具有辅助性作用。若无额外建模假设，实验数据本身不具备识别效力。当引入建模假设时，实验数据可增强其识别能力。若假设不成立，引入实验数据反而可能导致结果偏离真实值更远。基于此，本文提出两种可依据经济学理论或直观逻辑论证的处理响应假设。为应用这些假设，本文开发了一种新颖的两步识别方法，其核心在于界定时间关联函数——即短期与长期平均潜在结果之间的关系。该方法能为广义假设类别下的长期平均处理效应提供尖锐边界，并允许实验依从性存在缺陷，从而扩展了现有研究成果。