2023/05/27 by Eric V Strobl, Strobl, Eric V. · 1 citation
Arts and Humanities · Biochemistry, Genetics and Molecular Biology · Computer Science · #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Philosophy and History of Science #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2305.17574
openalex publication_date 2023/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Root causes of disease intuitively correspond to root vertices that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms designed to automatically detect root causes from data. Prior work defined patient-specific root causes of disease using an interventionalist account that only climbs to the second rung of Pearl's Ladder of Causation. In this theoretical piece, we climb to the third rung by proposing a counterfactual definition matching clinical intuition based on fixed factual data alone. We then show how to assign a root causal contribution score to each variable using Shapley values from explainable artificial intelligence. The proposed counterfactual formulation of patient-specific root causes of disease accounts for noisy labels, adapts to disease prevalence and admits fast computation without the need for counterfactual simulation.