Score-Based Causal Discovery with Temporal Background Information
Abstract
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.
Keywords
Cite
@article{arxiv.2502.06232,
title = {Score-Based Causal Discovery with Temporal Background Information},
author = {Tobias Ellegaard Larsen and Claus Thorn Ekstrøm and Anne Helby Petersen},
journal= {arXiv preprint arXiv:2502.06232},
year = {2025}
}