论文标题
非二进制治疗和作用的因果关系的概率
Probabilities of Causation with Nonbinary Treatment and Effect
论文作者
论文摘要
本文涉及估计治疗和效果不是二元的因果关系概率的问题。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）。在本文中，我们为各种因果关系的概率提供了理论上的界限。我们进一步讨论了我们的界限指导实际决策，并使用仿真研究来评估各种数据组合的界限。
This paper deals with the problem of estimating the probabilities of causation when treatment and effect are not binary. 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. In this paper, we provide theoretical bounds for all types of probabilities of causation to multivalued treatments and effects. We further discuss examples where our bounds guide practical decisions and use simulation studies to evaluate how informative the bounds are for various combinations of data.
