概率因果的通用样本量分析：基于delta方法的方法
General sample size analysis for probabilities of causation: a delta method approach
AI总结：
本文提出基于delta方法的通用样本量分析框架，用于确定概率因果分析所需的样本量，以稳定估计其界限。
摘要：
概率因果（PoCs），如必要性和充分性概率（PNS），是决策的重要工具，但通常无法点识别。现有研究通过实验和观察数据的组合推导了这些量的界限。然而，关于样本量分析的研究非常有限，即如何确定所需的实验和观察样本量以达到预期的误差范围。在本文中，我们提出了一种基于delta方法的通用样本量框架。我们的方法适用于目标PoC界限可以表示为实验和观察概率线性组合有限极值的设置。通过模拟研究，我们证明所提出的样本量计算方法能够稳定地估计这些界限。
英文摘要：
Probabilities of causation (PoCs), such as the probability of necessity and sufficiency (PNS), are important tools for decision making but are generally not point identifiable. Existing work has derived bounds for these quantities using combinations of experimental and observational data. However, there is very limited research on sample size analysis, namely, how many experimental and observational samples are required to achieve a desired margin of error. In this paper, we propose a general sample size framework based on the delta method. Our approach applies to settings in which the target bounds of PoCs can be expressed as finite minima or maxima of linear combinations of experimental and observational probabilities. Through simulation studies, we demonstrate that the proposed sample size calculations lead to stable estimation of these bounds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
