论文标题
一般基于设计的框架和随机实验的估计器
A General Design-Based Framework and Estimator for Randomized Experiments
论文作者
论文摘要
我们描述了一种基于设计的框架，用于在一般随机实验中绘制因果推断。因果效应定义为在单位级潜在结果函数上评估的线性功能。关于潜在结果函数的假设被编码为功能空间。这使得框架表达了框架，从而允许实验者提出和调查广泛的因果问题，包括有关干扰，这些问题以前无法使用基于设计的方法进行研究。我们描述了使用该框架定义的估计数并研究其属性的一类估计器。我们为无偏见和一致性提供了必要和充分的条件。我们还描述了一类保守的差异估计器，这些估计器有助于置信区间的构建。最后，我们提供了一些经验设置的例子，这些示例以前无法使用基于设计的方法来研究我们的方法在实践中的使用。
We describe a design-based framework for drawing causal inference in general randomized experiments. Causal effects are defined as linear functionals evaluated at unit-level potential outcome functions. Assumptions about the potential outcome functions are encoded as function spaces. This makes the framework expressive, allowing experimenters to formulate and investigate a wide range of causal questions, including about interference, that previously could not be investigated with design-based methods. We describe a class of estimators for estimands defined using the framework and investigate their properties. We provide necessary and sufficient conditions for unbiasedness and consistency. We also describe a class of conservative variance estimators, which facilitate the construction of confidence intervals. Finally, we provide several examples of empirical settings that previously could not be examined with design-based methods to illustrate the use of our approach in practice.
