论文标题
关于替代物在有限结果数据的有效估计效果中的作用
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
论文作者
论文摘要
在许多实验性和观察性研究中，感兴趣的结果通常很难或昂贵，即使在可识别时，也可以降低有效的样本量以估计平均治疗效果（ATE）。我们研究仅观察到只有替代结果而不是主要兴趣的单位上的数据才能提高ATE估计的精度。我们避免施加严格的代孕条件，这允许代理作为目标结果的理想替代者。取而代之的是，我们补充了对目标结果的可用（尽管有限），但对替代结果的观察结果充分，而没有任何假设，没有任何假设，而超出了无关的治疗分配，缺失以及相应的重叠条件。为了量化潜在的收益，我们在有或没有替代物的ATE估计中得出了效率界限的差异，无论是当压倒性或可比数量的单位缺少结果时。我们开发出强大的ATE估计和推理方法，以实现这些效率提高。我们通过研究工作培训的长期收入效应来证明收益。
In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment effects (ATEs) even when identifiable. We study how incorporating data on units for which only surrogate outcomes not of primary interest are observed can increase the precision of ATE estimation. We refrain from imposing stringent surrogacy conditions, which permit surrogates as perfect replacements for the target outcome. Instead, we supplement the available, albeit limited, observations of the target outcome with abundant observations of surrogate outcomes, without any assumptions beyond unconfounded treatment assignment and missingness and corresponding overlap conditions. To quantify the potential gains, we derive the difference in efficiency bounds on ATE estimation with and without surrogates, both when an overwhelming or comparable number of units have missing outcomes. We develop robust ATE estimation and inference methods that realize these efficiency gains. We empirically demonstrate the gains by studying long-term-earning effects of job training.
