论文标题
讨论“通过机器学习估算的因果参数的名义置信区间覆盖范围的几乎无假设测试”
Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning"
论文作者
论文摘要
我们祝贺作者的激动人心的论文，该论文引入了一个新颖的想法，以评估因果估计中的估计偏差。双重强大的估计器现在是因果推断中标准工具集的一部分，但是典型的分析以估计值和置信区间停止。作者提供了一种唯一类型的模型检查的方法，该方法使用户可以检查偏差是否足够小，相对于标准误差，这通常是置信区间可靠所必需的。
We congratulate the authors on their exciting paper, which introduces a novel idea for assessing the estimation bias in causal estimates. Doubly robust estimators are now part of the standard set of tools in causal inference, but a typical analysis stops with an estimate and a confidence interval. The authors give an approach for a unique type of model-checking that allows the user to check whether the bias is sufficiently small with respect to the standard error, which is generally required for confidence intervals to be reliable.
