Practical Causal Evaluation Metrics for Biological Networks

• URL: http://arxiv.org/abs/2511.12805v1
• Date: Sun, 16 Nov 2025 22:18:15 GMT
• Title: Practical Causal Evaluation Metrics for Biological Networks
• Authors: Noriaki Sato, Marco Scutari, Shuichi Kawano, Rui Yamaguchi, Seiya Imoto,
• Abstract summary: Estimating causal networks from biological data is a critical step in systems biology.<n>We developed a metric that takes the net effects of an intervention into account.<n>We found that our proposed metrics could identify a different algorithm as optimal compared to conventional metrics.
• Score: 2.0443308797642965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating causal networks from biological data is a critical step in systems biology. When evaluating the inferred network, assessing the networks based on their intervention effects is particularly important for downstream probabilistic reasoning and the identification of potential drug targets. In the context of gene regulatory network inference, biological databases are often used as reference sources. These databases typically describe relationships in a qualitative rather than quantitative manner. However, few evaluation metrics have been developed that take this qualitative nature into account. To address this, we developed a metric, the sign-augmented Structural Intervention Distance (sSID), and a weighted sSID that incorporates the net effects of the intervention. Through simulations and analyses of real transcriptomic datasets, we found that our proposed metrics could identify a different algorithm as optimal compared to conventional metrics, and the network selected by sSID had a superior performance in the classification task of clinical covariates using transcriptomic data. This suggests that sSID can distinguish networks that are structurally correct but functionally incorrect, highlighting its potential as a more biologically meaningful and practical evaluation metric.

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