标题： 基于设计的复杂实验估计理论 显示英文标题

标题： Design-based Estimation Theory for Complex Experiments

摘要： 本文考虑了在复杂实验设计的随机实验中处理效应的估计，包括单元之间存在干扰的情况。 我们为任意实验设计发展了一种基于设计的估计理论。 我们的理论有助于分析研究人员在实践中经常使用的许多设计-估计量对，并提供了一致估计渐近方差上限的方法。 我们从基于设计的角度提出了具有有利渐近性质的新估计量类。 此外，我们提出了一种标量的实验复杂性度量，该度量可以与估计量的基于设计的方差相关联。 我们使用基于实际网络实验的模拟数据集来展示我们估计量的性能，该实验研究了社交网络对保险采用的影响。 显示英文摘要

摘要： This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a design-based estimation theory for arbitrary experimental designs. Our theory facilitates the analysis of many design-estimator pairs that researchers commonly employ in practice and provide procedures to consistently estimate asymptotic variance bounds. We propose new classes of estimators with favorable asymptotic properties from a design-based point of view. In addition, we propose a scalar measure of experimental complexity which can be linked to the design-based variance of the estimators. We demonstrate the performance of our estimators using simulated datasets based on an actual network experiment studying the effect of social networks on insurance adoptions.