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Root-cause analysis is performed by evaluating observed effects against candidate causes to quantify causal strength. This process produces a structured attribution report containing causal chains, alternatives ruled out, confidence levels, and evidence anchors for debugging complex systems.

Causal modeling links events and state changes into a dependency graph. Provenance-aware analysis works by attaching evidence anchors and uncertainty metrics to every claim, ensuring that causal relationships are grounded in traceable data rather than assumptions.

Yes, root-cause attribution quantifies the strength of likely causes for observed effects. The output structure explicitly includes a strength metric for each identified cause alongside its confidence level and supporting evidence anchors.

The best way to structure causal reasoning is to generate a report containing causes, strengths, causal chains, alternatives ruled out, confidence, evidence anchors, and assumptions. This structured output supports both dependency mapping and planning.

Causal analysis requires listing alternatives ruled out and assumptions to validate the reasoning process. Documenting excluded causes and underlying assumptions ensures the root-cause attribution remains transparent, provenance-aware, and logically grounded.

No specific data formats are required; you need an observed effect and a list of candidate causes. The analysis processes these inputs to produce a grounded attribution report mapping the causal relationships and dependency graph.