This paper studies the estimation of long-term treatment effects though the combination of short-term experimental and long-term observational datasets. In particular, we consider settings in which only short-term outcomes are observed in an experimental sample with exogenously assigned treatment, both short-term and long-term outcomes are observed in an observational sample where treatment assignment may be confounded, and the researcher is willing to assume that the causal relationships between treatment assignment and the short-term and long-term outcomes share the same unobserved confounding variables in the observational sample. We derive the efficient influence function for the average causal effect of treatment on long-term outcomes in each of the models that we consider and characterize the corresponding asymptotic semiparametric efficiency bounds.

翻译：本文研究通过短期试验和长期观察数据集相结合对长期治疗效果的估计。我们尤其考虑在外部分配治疗实验样本中只观察到短期结果的环境,在可能令人困惑的观察样本中观察短期和长期结果,研究者愿意假设,在观察样本中,治疗任务与短期和长期结果之间的因果关系具有相同的未观察到的混杂变量。我们从我们所考虑的每个模型中得出处理对长期结果平均因果关系的有效影响功能。