论文标题
因果关系的概率：观察数据的作用
Probabilities of Causation: Role of Observational Data
论文作者
论文摘要
因果关系的概率在现代决策中起着至关重要的作用。珍珠定义了因果关系的三个二元概率，必要性和充分性的概率（PNS），足够的概率（PS）和必要性的概率（PN）。然后，使用实验和观察数据的组合将这些概率通过天和珍珠界定。但是，在实践中并不总是可用的观察数据。在这种情况下，田和珍珠定理使用纯实验数据提供了有效但效率较低的界限。在本文中，我们讨论了观察数据值得考虑提高界限质量的条件。更具体地说，我们通过假设观测分布在其可行的间隔上均匀分布来定义边界的预期改善。我们进一步将提出的定理应用于Li和Pearl定义的单位选择问题。
Probabilities of causation play a crucial role in modern decision-making. Pearl defined three binary probabilities of causation, the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN). These probabilities were then bounded by Tian and Pearl using a combination of experimental and observational data. However, observational data are not always available in practice; in such a case, Tian and Pearl's Theorem provided valid but less effective bounds using pure experimental data. In this paper, we discuss the conditions that observational data are worth considering to improve the quality of the bounds. More specifically, we defined the expected improvement of the bounds by assuming the observational distributions are uniformly distributed on their feasible interval. We further applied the proposed theorems to the unit selection problem defined by Li and Pearl.
