Temporal background information can improve causal discovery algorithms by orienting edges and identifying relevant adjustment sets. We develop the Temporal Greedy Equivalence Search (TGES) algorithm and terminology essential for score-based causal discovery with tiered background knowledge. TGES learns a restricted Markov equivalence class of directed acyclic graphs (DAGs) using observational data and tiered background knowledge. To construct TGES we formulate a scoring criterion that accounts for tiered background knowledge. We establish theoretical results for TGES, stating that the algorithm always returns a tiered maximally oriented partially directed acyclic graph (tiered MPDAG) and that this tiered MPDAG contains the true DAG in the large sample limit. We present a simulation study indicating a gain from using tiered background knowledge and an improved precision-recall trade-off compared to the temporal PC algorithm. We provide a real-world example on life-course health data.

翻译：时序背景信息可通过定向边和识别相关调整集来改进因果发现算法。本文开发了时序贪婪等价搜索（TGES）算法及配套术语体系，为基于分数的分层背景知识因果发现奠定基础。TGES利用观测数据和分层背景知识学习有向无环图（DAG）的受限马尔可夫等价类。为构建TGES，我们建立了考虑分层背景知识的评分准则。我们证明了TGES的理论性质：该算法始终返回分层最大定向部分有向无环图（分层MPDAG），且该分层MPDAG在大样本极限下包含真实DAG。仿真研究表明，相较于时序PC算法，使用分层背景知识能提升性能并改善精确率-召回率的权衡关系。我们以生命历程健康数据为例展示了实际应用效果。