论文标题
基于设计的因果推断的浆果 - 范围，可能有不同的治疗水平和不同的组大小
Berry-Esseen bounds for design-based causal inference with possibly diverging treatment levels and varying group sizes
论文作者
论文摘要
Neyman（1923/1990）引入了随机模型，该模型包含根据实验的设计来定义因果效应的潜在结果和大样本推理的框架。但是，该框架的现有理论远非完整，尤其是当治疗水平分歧和治疗组大小变化时。我们在随机模型中提供了统一的统计推断，并提供了一般治疗组大小的统计推断。我们根据线性置换统计量来制定估计量，并根据Stein的方法使用结果来得出估计器的线性和二次函数上的各种浆果。这些新的Berry - Esseen Bounds是基于设计的因果推断的基础，并可能存在分歧的治疗水平和不同感兴趣的因果参数。我们还通过提出新的方差估计器来填补重要的空白，以实验可能有很多治疗水平而没有复制的实验。配备了新开发的结果，在一般环境中基于设计的因果推断变得更加方便，并具有更强的理论保证。
Neyman (1923/1990) introduced the randomization model, which contains the notation of potential outcomes to define causal effects and a framework for large-sample inference based on the design of the experiment. However, the existing theory for this framework is far from complete, especially when the number of treatment levels diverges and the treatment group sizes vary. We provide a unified discussion of statistical inference under the randomization model with general treatment group sizes. We formulate the estimator in terms of a linear permutation statistic and use results based on Stein's method to derive various Berry--Esseen bounds on the linear and quadratic functions of the estimator. These new Berry--Esseen bounds serve as the basis for design-based causal inference with possibly diverging treatment levels and a diverging number of causal parameters of interest. We also fill an important gap by proposing novel variance estimators for experiments with possibly many treatment levels without replications. Equipped with the newly developed results, design-based causal inference in general settings becomes more convenient with stronger theoretical guarantees.
