论文标题
使用观察数据和实验数据估计治疗效果，并具有非重叠支持
Estimating Treatment Effects Using Observational Data and Experimental Data with Non-overlapping Support
论文作者
论文摘要
当估计治疗效果时，黄金标准是进行随机实验，然后与治疗组和对照组相关的对比度。但是，在许多情况下，与目标人群的规模相比，随机实验的规模要小得多，或者伴随着某些道德问题，因此很难实施。因此，研究人员通常依靠观察数据来研究因果关系。不利的一面是，不满意的假设，验证观察数据的使用的关键很难验证，几乎总是违反。因此，应格外小心对观察数据得出的任何结论进行进一步分析。鉴于观察数据的丰富性和实验数据的实用性，研究人员希望开发可靠的方法来结合两者的强度。在本文中，我们考虑了一个环境，其中观察数据包含感兴趣的结果以及替代结果，而实验数据仅包含替代结果。我们提出了一个简单的估计器，以使用观察数据和实验数据估算关注的平均治疗效果。
When estimating treatment effects, the golden standard is to conduct a randomized experiment and then contrast outcomes associated with the treatment group and the control group. However, in many cases, randomized experiments are either conducted with a much smaller scale compared to the size of the target population or accompanied with certain ethical issues and thus hard to implement. Therefore, researchers usually rely on observational data to study causal connections. The downside is that the unconfoundedness assumption, the key to validate the use of observational data is hard to verify and almost always violated. Hence, any conclusion drawn from observational data should be further analyzed with great care. Given the richness of observational data and usefulness of experimental data, researchers hope to develop credible method to combine the strength of the two. In this paper, we consider a setting where the observational data contain the outcome of interest as well as a surrogate outcome while the experimental data contain only the surrogate outcome. We propose a simple estimator to estimate the average treatment effect of interest using both the observational data and the experimental data.
