rca-causal-inference

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Mathematical foundations of root cause analysis using Judea Pearl's Structural Causal Models (SCMs), do-calculus, counterfactual reasoning, Bayesian networks for fault diagnosis, graph-theoretic centrality for fault localization, information-theoretic methods (transfer entropy, mutual information), and Granger causality. Use when the incident involves quantifiable observational data, when you need to distinguish correlation from causation, when you want to compute "what would have happened if we had done X instead," or when building automated fault-detection systems that reason about cause rather than pattern-match on symptoms.