Causal Inference for Natural Product Therapeutics: Moving Beyond Association Toward Mechanism, Intervention Logic, and Translational Confidence

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Natural product therapeutics generate diverse evidence from phytochemical profiling, molecular docking, bioactivity screening, target prediction, network pharmacology, omics studies, traditional-use records, observational analyses, experimental models, and safety surveillance. These evidence streams are valuable for discovery but frequently support association or prediction rather than causal conclusions about mechanism or therapeutic benefit. This article proposes an original causal AI framework for organizing natural product evidence around explicit causal questions, intervention definitions, comparator strategies, outcomes, causal estimands, directed acyclic graphs, structural causal reasoning, potential outcomes, confounding control, mediation, effect modification, target-trial logic, experimental perturbation, and evidence triangulation. The framework separates hypothesis-generating signals from evidence capable of supporting progressively stronger mechanistic or intervention-oriented interpretations. It also introduces translational confidence criteria covering product identity, temporal ordering, exposure relevance, mechanistic plausibility, bias sensitivity, safety, replication, external validity, transportability, expert review, and transparent claim boundaries. Causal AI is positioned as a method for assumption mapping, evidence classification, counterfactual reasoning, validation planning, and uncertainty management rather than as an autonomous means of proving biological causation, clinical efficacy, or safety. The principal contribution is a structured pathway through which heterogeneous natural product data can be converted into testable causal hypotheses and validation priorities while preserving clear distinctions among association, prediction, causal inference, mechanistic evidence, experimental confirmation, and clinical effectiveness.