A generator-matrix causal-inference framework separates measurable aging biomarkers from mortality-driving latent...

A central challenge in computational geroscience is to distinguish molecular quantities that predict mortality from those that causally drive it. Epigenetic clocks and aging biomarkers are increasingly used as if they were that mechanism, yet this is rarely tested directly. This distinction also bears on competing theories of aging: damage/reliability (A), hyperfunction/mTOR-IIS (B-1), and information loss (B-2). Although individual aging proteins have been tested piecemeal, no study has asked, in one framework, what fraction of mortality is measurable, whether it is causal, and whether it is reversible. Using only public, de-identified data, we evaluate this three ways. First, a Markov generator-matrix model of hallmark-load dynamics with death as an absorbing state, fitted by Bayesian inference through a joint biomarker-and-mortality likelihood to NHANES with linked mortality (n=23,844) and replicated in the Health and Retirement Study (HRS), decomposes Gompertz acceleration into visible (measured-biomarker-driven) and latent components. Second, a positive-control-calibrated, two-platform cis-pQTL Mendelian-randomization and colocalization design (UKB-PPP, deCODE) against parental-lifespan GWAS tests whether the latent's measurable components are causal. Third, a clock battery (Horvath, chronological; DamAge, causality-enriched damage) tests reversibility in cellular reprogramming. Within the model, ~92% of Gompertz acceleration is assigned to a latent component not captured by measured blood-biomarker axes (NHANES 92.5%, HRS 91.6%); the latent is partly encoded in DNA-methylation signatures but not transcription. The known causal proteins are detected (LPA p=9×10^-12; IL6R p=2.8×10^-5), yet the latent's components, across inflammatory, renal and growth-signalling (IGFBP3, IGF-1) axes, are null and do not colocalize on either platform. Reprogramming reverses the chronological clock (-11 to -22 yr) but not the causality-enriched damage clock. The model-inferred mortality-driving component is largely latent to accessible biomarkers; its measurable molecular proxies show no supported causal effect where the design detects known causes; and the causality-enriched damage-clock signal is resistant to partial reprogramming.