Natural products can produce rich phenotypic, transcriptional, morphological, and proteomic responses while leaving the molecular cause of those responses uncertain. Contemporary target-discovery approaches increasingly connect compound perturbations with omics signatures, predicted targets, proteome-wide engagement, genetic sensitivity, and pathway effects, but these observations do not carry equivalent causal meaning. This article develops a proposed causal evidence architecture for separating target nomination from progressively stronger target validation. The architecture distinguishes evidence that identifies plausible targets from evidence of physical engagement, functional dependency, phenotype mediation, and counterfactual rescue. It further treats orthogonality as the ability of independent experiments to eliminate different alternative explanations rather than as the simple accumulation of concordant assays. Particular attention is given to natural products, for which polypharmacology, covalent reactivity, probe derivatization, context-dependent exposure, and downstream network responses can make apparently coherent target narratives misleading. The proposed architecture therefore preserves uncertainty, conflicting observations, multiple-target explanations, and assay non-detectability as explicit evidence states rather than forcing every dataset toward a single mechanistic conclusion. Causal confidence is intended to inform what experiment or development decision should follow, not to provide a universal numerical validation score. The framework does not establish prospective predictive performance, therapeutic efficacy, or clinical relevance. Instead, it provides an evidence-bounded structure for asking whether a nominated protein is merely associated with a natural-product response, physically engaged by the compound, functionally necessary for that response, or sufficiently supported to justify stronger mechanistic interpretation.
