Checklist
Make causal diagrams traceable to evidence
Traceability separates hypotheses from supported links.
When it fits
- When a systems map looks authoritative but the origin of its arrows is unclear.
When to avoid it
- Leverage-point and information-value heuristics are prompts, not guaranteed effect sizes. Test interventions, monitor side effects and stop when evidence changes.
Checklist
- Choose decision-critical links.
- Attach evidence or provenance to each.
- Mark assumptions and disagreement.
- Prioritize weak high-leverage links for testing.
Why it matters
For each decision-critical causal link, record whether it comes from data, experiment, literature, domain observation or assumption. Mark disputed and weak links visibly.
An example
The map labels 'approval delay → batching' as a domain hypothesis and links 'WIP → cycle time' to measured flow data.
Check your result
A reviewer can tell which arrows are measured, sourced or speculative.
Keep this limit in mind
- Leverage-point and information-value heuristics are prompts, not guaranteed effect sizes. Test interventions, monitor side effects and stop when evidence changes.
Connected ideas
Use beforeTrack leading signals tied to the mechanism
Evidence and sources
Supports
A review of causal-loop-diagram publications found incomplete reporting of development methods and causal-link sources, supporting explicit provenance for qualitative causal maps.
Transparency improves inspectability but does not establish that a stated causal link is true.
Strengthening a Weak Link: Transparency of Causal Loop Diagrams—Current State and Recommendations · Abstract