A cellular phenotype rarely identifies its molecular cause uniquely. Regulatory redundancy, resource competition, compensatory interactions, alternative network configurations, and incomplete measurement can allow distinct molecular states to generate observations that are difficult or impossible to distinguish under a given assay. This creates an identifiability problem that is deeper than ordinary uncertainty in parameter estimation: the scientific question is whether competing molecular explanations are distinguishable at all from the available observations and, if not, what additional evidence could separate them. This article develops a non-empirical methodological framework for analyzing that problem in computational biology. It distinguishes mechanistic equivalence, defined relative to specified observables and experimental conditions, from structural and practical identifiability in mathematical network models, and then links these concepts to perturbation-based causal discrimination. The framework treats model fit, predictive accuracy, perturbational responsiveness, and causal specificity as different evidentiary achievements rather than interchangeable indicators of mechanistic truth. Evidence from single-cell perturbation studies, gene-regulatory-network inference, multimodal profiling, dynamic-model calibration, and identifiability analysis shows that additional measurements can narrow an explanation space without necessarily producing a unique causal account. The central implication is that causal specificity should be claimed only when competing explanations generate discriminably different predictions under explicitly defined observations or interventions and those differences survive relevant measurement, model, and biological-context limitations. Where such discrimination fails, mechanistic equivalence should be retained rather than resolved by model complexity or interpretive preference.
