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适用于一般处理类型的 Fisher 因果随机化检验AI 解读

We extend Fisher's randomization test (FRT) to test conditional independence between observed outcomes and treatments given covariates in both randomized experiments and observational studies, with no restriction on the variable type of treatments. Under a generalized unconfoundedness assumption, we provide causal identification for this hypothesis. Our approach requires neither the no-interference nor the positive overlap assumption, making it a widely applicable tool for…

随机实验中样本局部平均处理效应的两阶段推断AI 解读

In a given randomized experiment, individuals are often volunteers and can differ in important ways from a population of interest. It is thus of interest to focus on the sample at hand. This paper focuses on inference about the sample local average treatment effect (LATE) in randomized experiments with non-compliance. We present a two-stage procedure that provides asymptotically correct coverage rate of the sample LATE in randomized experiments. The procedure uses a…

完全随机实验中样本依从者平均因果效应的推断AI 解读

In randomized experiments with non-compliance scholars have argued that the complier average causal effect (CACE) ought to be the main causal estimand. The literature on inference of the complier average treatment effect (CACE) has focused on inference about the population CACE. However, in general individuals in the experiments are volunteers. This means that there is a risk that individuals partaking in a given experiment differ in important ways from a population of…

完全随机化与计算机辅助平衡设计下的样本依从者平均因果效应推断AI 解读

Non-compliance is common in real world experiments. We focus on inference about the sample complier average causal effect, that is, the average treatment effect for experimental units who are compliers. We present three types of inference strategies for the sample complier average causal effect: the Wald estimator, regression adjustment estimators and model-based Bayesian inference. Because modern computer assisted experimental designs have been used to improve covariate…