Ablation-Restoration-Recovery as a Causal Verification Sequence in Multiscale Dynamical Systems

Ablation is widely used to test whether a candidate mechanism contributes to an observed outcome, but intervention sensitivity alone is not equivalent to causal verification. We formulate causal verification as a linked evidence sequence in which a versioned baseline is followed by controlled ablation or perturbation, state-response measurement, restoration, and time-resolved recovery. The framework explicitly represents compensation, collateral perturbation, observer dependence, history, cross-scale mapping, restoration completeness, and unresolved causal equivalence. We prove constructive insufficiency results showing that a large ablation effect can arise from collateral damage, while a necessary mechanism can appear irrelevant when compensation masks its loss. We then define restoration-consistency and recovery-consistency criteria and require causal promotion to survive held-out or independent intervention-restoration pairs. Three controlled dynamical benchmarks illustrate the logic: target restoration exposes a false-positive ablation attribution, compensation breaking exposes a false-negative attribution, and two distinct perturbation routes converge after restoration of a shared target. The method does not claim universal causal identification; it provides an auditable evidence architecture that separates sensitivity, restoration, recovery, compensation and non-uniqueness in multiscale dynamical systems.