Probabilistic logic programs are logic programs where some facts hold with a specified probability. Here, we investigate these programs with a causal framework that allows counterfactual queries. Learning the program structure from observational data is usually done through heuristic search relying on statistical tests. However, these statistical tests lack information about the causal mechanism generating the data, which makes it unfeasible to use the resulting programs for counterfactual reasoning. To address this, we propose a language fragment that allows reconstructing a program from its induced distribution. This further enables us to learn programs supporting counterfactual queries.

翻译：概率逻辑程序是一种逻辑程序，其中某些事实以特定概率成立。本文在允许反事实查询的因果框架下研究这类程序。从观测数据中学习程序结构通常依赖于基于统计检验的启发式搜索。然而，这些统计检验缺乏关于数据生成因果机制的信息，导致无法将所得程序用于反事实推理。为解决这一问题，我们提出了一种语言片段，该片段允许从程序诱导的分布中重建程序，进而使我们能够学习支持反事实查询的程序。