论文标题
因果关系的概率：实验和观察样品的足够大小
Probabilities of Causation: Adequate Size of Experimental and Observational Samples
论文作者
论文摘要
因果关系的概率通常用于解决决策问题。田和珍珠的尖锐边界是必要和充分性（PNS）的概率，足够概率（PS）的概率以及使用实验和观察数据的必要性（PN）的概率（PN）。假设是，一个人拥有足够大的样本，以允许对实验和观察分布进行准确的估计。在这项研究中，我们提出了一种指定给定置信区间（CI）时确定此类估算所需的样本量的方法。我们通过模拟进一步显示，提出的样本量对PN的边界进行了稳定的估计。
The probabilities of causation are commonly used to solve decision-making problems. Tian and Pearl derived sharp bounds for the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN) using experimental and observational data. The assumption is that one is in possession of a large enough sample to permit an accurate estimation of the experimental and observational distributions. In this study, we present a method for determining the sample size needed for such estimation, when a given confidence interval (CI) is specified. We further show by simulation that the proposed sample size delivered stable estimations of the bounds of PNS.
