论文标题
概率树中因果推理的算法
Algorithms for Causal Reasoning in Probability Trees
论文作者
论文摘要
概率树是因果生成过程中最简单的模型之一。它们具有干净的语义，与因果贝叶斯网络不同，它们可以代表特定于上下文的因果关系，这对于例如因果诱导。但是，他们很少受到AI和ML社区的关注。在这里，我们介绍了涵盖整个因果层次结构（关联，干预和反事实）的离散概率树中因果推理的具体算法，并在任意命题和因果事件上运作。我们的工作将因果推理的领域扩展到非常一般的离散随机过程。
Probability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal induction. Yet, they have received little attention from the AI and ML community. Here we present concrete algorithms for causal reasoning in discrete probability trees that cover the entire causal hierarchy (association, intervention, and counterfactuals), and operate on arbitrary propositional and causal events. Our work expands the domain of causal reasoning to a very general class of discrete stochastic processes.
