论文标题
从有限人群数据中的因果关系的学习概率
Learning Probabilities of Causation from Finite Population Data
论文作者
论文摘要
本文讨论了在有限的人群数据下学习亚种群因果关系的概率的问题。 Tian和Pearl得出了三种基本因果关系的紧密界限，必要性和充分性（PNS）的概率（PNS），概率（PS）和必要性（PN）的概率是由Tian和Pearl得出的。但是，获得每个亚群的边界需要每个亚种群的实验和观察分布，这通常不切实际地估算给定有限的人群数据。我们提出了一个机器学习模型，该模型有助于学习有限的人群数据，以了解亚群的因果关系的概率。我们通过一项模拟研究进一步证明，机器学习模型能够学习32768个亚群的PNS的界限，仅了解有限的人群数据中的大约500个。
This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data. The tight bounds of three basic probabilities of causation, the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN), were derived by Tian and Pearl. However, obtaining the bounds for each subpopulation requires experimental and observational distributions of each subpopulation, which is usually impractical to estimate given finite population data. We propose a machine learning model that helps to learn the bounds of the probabilities of causation for subpopulations given finite population data. We further show by a simulated study that the machine learning model is able to learn the bounds of PNS for 32768 subpopulations with only knowing roughly 500 of them from the finite population data.
