Evaluating Directed Acyclic Graphs with DAGMetrics: Insights from Tuber and Soil Microbiome Data

Understanding and evaluating directed acyclic graphs (DAGs) is crucial for causal discovery, particularly in high-dimensional and small-sample datasets such as microbial abundance data. This study introduces DAGMetrics, an R package designed to comprehensively evaluate and compare DAGs. The package provides descriptive and comparative metrics, streamlining the assessment of outputs from various structure learning algorithms. It was applied to datasets generated for potato tubers and soils from different terroirs (continental and island) and stages (at harvest and post-harvest). Using a comprehensive set of descriptive and comparative metrics, DAGMetrics facilitated model selection by identifying balanced and robust DAGs. The PC algorithm with Spearman correlation produced DAGs with moderate complexity and high stability across scaling and transformation setups. Additionally, the package enabled detailed exploration of the Markov blanket space, revealing small Markov blankets (up to seven nodes) and numerous isolated nodes. Identified matching edges between Markov blankets across different terroirs and stages aligned with known microbial interactions, highlighting the package’s utility in facilitating the discovery of biologically meaningful relationships. This study illustrates the utility of DAGMetrics in providing objective and reproducible tools for DAG evaluation along with its potential application in agronomic and other domains involving complex structured data.