Consensus Enhances Individual Causal Models: a Use Case on Lung Cancer Driven by Cellular Pathways

Discovering reliable cause-and-effect relationships in real-world medical data is an open challenge. Classical Causal Discovery (CD) algorithms used to solve this task rely on strict assumptions that are rarely met in complex realworld scenarios with limited expert knowledge -the functional form of the causal relationships, the data distribution, the causal sufficiency. Thus, the reliability of CD algorithms can significantly drop, compromising the interpretability of the results and the trustworthiness of downstream decision-making. To overcome these limitations, we introduce the concept of consensus causal model to combine various CD algorithms and enhance their accuracy. Our consensus model can be efficiently constructed from a set of heterogeneous causal graph objects through a homogenisation step, ensuring semantic compatibility with the original edge definitions and enabling meaningful information exchange. To showcase the proposed method, we analyze a lung cancer dataset combining patient-level information such as smoking habits and age, and we study their effect on the onset and development of the disease, the tumor stage, and cellular pathway mutations. By applying multiple classical CD algorithms, we observe significant structural inconsistencies and heterogeneity across individual graphs. We demonstrate that the consensus causal model, unlike the individual models, effectively aggregates the strengths of each algorithm while mitigating their uncertainties. The resulting model reveals biologically validated causal relationships between risk factors, mutations, and pathways that isolated algorithms fail to capture, thereby underscoring the value of consensus causal modelling as a robust alternative to singlemodel selection for causal discovery.

Arnaud Lang, Rodrigo Henrique Ramos, Safaa Al-Ali, Mohammad Reza Mousavi, Anna Calissano, et al.. Consensus Enhances Individual Causal Models: a Use Case on Lung Cancer Driven by Cellular Pathways. CMSB 2026 - 24th International Conference on Computational Methods in Systems Biology, Jul 2026, Lisbon, Portugal. ⟨hal-05620648⟩ (lien externe)