Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference while maintaining exact answers. In this paper, we show how lifting can be applied to causal inference in partially directed graphs, i.e., graphs that contain both directed and undirected edges to represent causal relationships between random variables. We present partially directed parametric causal factor graphs (PPCFGs) as a generalisation of previously introduced parametric causal factor graphs, which require a fully directed graph. We further show how causal inference can be performed on a lifted level in PPCFGs, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships.

翻译：提升（Lifting）利用不可区分个体的代表性，在概率关系模型（表示为参数因子图）中利用对称性以加速推理，同时保持精确答案。本文展示了如何将提升技术应用于部分有向图（即包含有向边和无向边以表示随机变量间因果关系的图）中的因果推断。我们提出部分有向参数因果因子图（PPCFGs）作为先前提出的参数因果因子图（要求完全有向图）的推广。我们进一步展示了如何在PPCFGs的提升层次上进行因果推断，从而将提升因果推断的适用性扩展到更广泛的模型类别，这些模型对因果关系的先验知识要求更低。