Retrospective causal attribution evaluates whether altering an earlier event would have prevented an observed outcome. Observational data determine marginal potential-response probabilities at fixed inputs while leaving cross-world joint distributions unspecified. Conditional on a complete factual record in a finite Markovian structural causal model, independence across mechanisms is preserved and each unobserved conditional response distribution is restricted to a capped probability simplex. Every collection of conditional distributions satisfying these local bounds is jointly attainable within a single compatible structural causal model. Optimizing the counterfactual expectation over the resulting product domain yields sharp attribution bounds for multistage interventions and mediated effects, and compatible response-vector polytopes extend these bounds to structural response restrictions. Exact algorithms for causal chains and general directed acyclic graphs, consistent plug-in endpoint estimators, and finite-sample and asymptotic confidence intervals are developed and illustrated using synthetic reactor experiments and health survey data.

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