logic-causality-mapping

Root cause tracing works backward from an observed effect to identify proximate cause, root cause, and contributing factors. You provide the observed outcome, including timing and measurements, and the analysis separates these layers to reveal the actual driver.

Impact mapping works forward from a proposed change to identify direct effects, downstream consequences, and cascade risks. By tracing dependencies, it predicts what breaks under change and flags irreversible effects before you act.

Counterfactual reasoning evaluates whether a causal claim holds by testing what must be true for a plan to succeed. It validates hypotheses by checking if the outcome changes when the proposed cause is removed or altered.

Yes, dependency mapping applies to complex software systems by tracing what must be true for a plan to succeed. It identifies dependencies and evaluates whether causal claims hold under counterfactual testing.

Structured incident analysis requires making causal scope explicit and producing outputs that separate proximate cause, root cause, dependencies, and evidence. This replaces misleading 'it happened then it caused' stories with explicit causal structure.

Causal mapping is less effective when you lack concrete observations or measurable outcomes. It requires choosing an appropriate mode and making causal scope explicit, so isolated bugs without systemic dependencies may not benefit from this approach.