Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. Even though lifting is a well-established technique for the task of probabilistic inference in relational domains, it has not yet been applied to the task of causal inference. In this paper, we show how lifting can be applied to efficiently compute causal effects in relational domains. More specifically, we introduce parametric causal factor graphs as an extension of parametric factor graphs incorporating causal knowledge and give a formal semantics of interventions therein. We further present the lifted causal inference 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 our empirical evaluation, we demonstrate the effectiveness of our approach.

翻译：提升推断通过使用不可区分对象的代表来利用概率图模型中的对称性，从而在保持精确答案的同时加速查询回答。尽管提升推断已作为关系域中概率推断任务的成熟技术得到充分发展，但尚未应用于因果推断任务。本文展示了如何将提升推断应用于高效计算关系域中的因果效应。具体而言，我们引入参数化因果因子图作为参数化因子图的扩展，融入因果知识并给出其中干预的形式化语义。我们还提出了提升因果推断算法，用于在提升层面计算因果效应，从而相较于命题级推断（例如因果贝叶斯网络中的推断）大幅加速因果推断。实验评估证明了我们方法的有效性。