用提升推理加速关系型数据的因果推断，保持精确性。
Lifted Causal Inference
- 用代表性对象处理相似实体，实现因果推断的高效计算。
- 相比传统贝叶斯网络，推理速度显著提升，结果完全精确。
- 支持部分已知因果关系，降低对先验知识的要求，适用更广。
提升推理通过利用概率图模型中不可区分对象的特性，使用代表性对象来加速查询回答，同时保持结果精确。本文展示如何将提升推理应用于关系领域中的因果效应计算。具体而言，引入参数化因果因子图（PCFGs）以在提升模型中融入因果知识，并给出干预操作的形式语义。进一步提出提升因果推断（LCI）算法，在提升层面计算因果效应，相比命题推理（如因果贝叶斯网络）显著加速。此外，提出部分有向参数化因果因子图（PD-PCFGs）作为PCFGs的推广，用于处理不完整因果知识，并将LCI扩展至在PD-PCFG上执行提升因果推断，从而扩大了提升因果推断的应用范围，降低对因果关系先验知识的需求。
原文摘要 · Abstract (English)
Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. In this article, we show how lifting can be applied to efficiently compute causal effects in relational domains. More specifically, we introduce parametric causal factor graphs (PCFGs) to incorporate causal knowledge in lifted models and give a formal semantics of interventions therein. We further present the Lifted Causal Inference (LCI) algorithm to compute causal effects on a lifted level, thereby drastically speeding up causal inference compared to propositional inference, e.g., in causal Bayesian networks. In addition, we present partially directed parametric causal factor graphs (PD-PCFGs) as a generalisation of PCFGs to handle partial causal knowledge and extend LCI to perform lifted causal inference in a PD-PCFG, 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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