Target validation is often described as a progressive accumulation of evidence, yet pharmaceutical research frequently treats qualitatively different claims as though they were interchangeable. Statistical association, molecular prediction, experimentally observed mediation, disease modification, and successful therapeutic intervention answer different scientific questions and are vulnerable to different sources of error. Collapsing these levels can cause an associated locus to be presented as a causal gene, a perturbed cellular phenotype to be interpreted as disease modification, or a genetically supported mechanism to be assumed pharmacologically actionable. This article develops a proposed causal target-validation model that preserves these distinctions while allowing evidence to be integrated across human genetics, functional perturbation, context-specific omics, disease models, and pharmacology. The model organizes target validation into separable evidentiary levels connected by conditional inference gates rather than by an automatic linear progression. It further introduces a causal-threat register covering confounding, pleiotropy, linkage, context mismatch, perturbation fidelity, direction of effect, and genetic-to-pharmacological analogy. Target advancement is consequently treated as a bounded, reversible decision based on the weakest unresolved causal link rather than on a single score or performance measure. The proposed model is conceptual and has not been empirically validated as a predictive or operational decision system. Its usefulness will depend on the quality, independence, biological relevance, and transportability of the evidence supplied to each component. By separating what evidence supports from what it cannot establish, the model may improve the design, interpretation, and documentation of target-validation programs while reducing premature claims of therapeutic causation.