How to use: Domino Effect Graph

Domino Graph visualises causal chains — how one macro event triggers the next. Each node is an event; each edge is a causal link with a probability score.

What you're looking at
Click any node to see its description, trigger probability, and what it causes downstream.
Chain selector — switch between different macro scenarios (e.g. Inflation Chain vs. Property Stress Chain).
Edge labels — probabilities on each link. A 90% edge means that causal step is nearly certain given the upstream trigger.
The final nodes in a chain are the investable outcomes — asset class moves or policy responses.
Key metrics
Trigger Probability
Likelihood that this node fires given that the upstream node has fired. Compound the probabilities to get end-to-end chain probability.
Chain Confidence
Overall confidence in the complete causal chain. Based on historical episode similarity and current data alignment.
Pro tips
💡Multiply probabilities along the chain to get the end-to-end scenario probability. E.g. 85% × 80% × 75% = ~51% for a 3-step chain.
💡Use the Domino Graph before AI Insights — it gives you the causal structure to ask better questions.
Full User Guide
Glossary, workflows, all features
Macro RegimeLate-Cycle Tightening→Shifting towardStagflation Risk72% conf.
• Inflation re-accelerating above Fed target• Labor market resilience limits rate cut probability• Growth slowing in key economies (Germany, China)

Domino Effect Graph

Event cascade — how one shock propagates

1
DETECT
2
IMPACT
3
HISTORY
4
ACT
Trigger Event

US CPI beats expectations — core inflation sticky at 3.2%

Mini Map
React Flow

Tap a node to see reasoning and causal links