论文标题
通过关系环化学习与周期的关系因果模型
Learning Relational Causal Models with Cycles through Relational Acyclification
论文作者
论文摘要
在涉及互连单元之间相互影响或因果关系的现实现象中，平衡状态通常用图形模型中的周期表示。一类表达的图形模型，关系因果模型，可以代表和理由关于表现出此类周期或反馈回路的复杂动态系统。从观察数据中学习因果模型的现有循环因果发现算法假定，数据实例是独立的且分布相同的，这使得它们不适合关系因果模型。同时，关系因果模型的因果发现算法假定超循环。在这项工作中，我们研究了必要的充分条件，在这些条件下，基于约束的关系因果发现算法是合理的，并且对于环状关系因果模型而言是完整的。我们介绍了关系循环化，这是专门为关系模型设计的操作，该模型可以推理有关环关系因果模型的可识别性。我们表明，在关系周期化和$σ$ - 信仰的假设下，关系因果发现算法RCD（Maier等人，2013年）是合理的，并且对于环状模型而言是完整的。我们提出了实验结果以支持我们的主张。
In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of graphical models, relational causal models, can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal discovery algorithms for learning causal models from observational data assume that the data instances are independent and identically distributed which makes them unsuitable for relational causal models. At the same time, causal discovery algorithms for relational causal models assume acyclicity. In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models. We show that under the assumptions of relational acyclification and $σ$-faithfulness, the relational causal discovery algorithm RCD (Maier et al. 2013) is sound and complete for cyclic models. We present experimental results to support our claim.
