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| Criterion |  Operations Causal Factor Analysis |  Product Discovery Experiment Canvas |  Product Strategy DIBB |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
Purposedifferent | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | High | Low | Low | Medium |
Timedifferent | 2-6 h | 30-60 min | 1-2 h | 45-60 min |
Participantsdifferent | 3-10 | 1-5 | 2-8 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | CausalityIncidentRoot causeTimeline | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis | AssumptionsRiskExperimentsValidation |