将提升技术引入部分有向因果图，实现更高效精确的因果推断。
Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
- 提出部分有向参数因果因子图（PPCFG），兼容有向与无向边
- 在保持答案精确的前提下，实现因果推断的层级化加速
- 适合对因果结构不确定但需高效推断的研究者
提升方法利用不可区分个体的代表性，通过概率关系模型中的对称性——即参数因子图——加速推理并保持结果精确。本文展示如何将提升应用于包含有向与无向边的混合图中的因果推断，这类图可表示随机变量间的因果关系。我们提出部分有向参数因果因子图（PPCFG）作为先前参数因果因子图的推广，后者要求完全有向图。进一步地，我们展示了如何在PPCFG上进行层级化的因果推断，从而将提升因果推断的应用范围扩展到更广泛且对因果关系先验知识较少的模型。
原文摘要 · Abstract (English)
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.
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