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A crucial challenge in using quantitative risk models to inform decisions is to find an interface that shows the relationship between the model and the real world it represents. A model should provide transparency and insight. Too much mathematics will occlude that clarity for all but the most mathematically articulate, but too little risks turning the model into a black box and abandoning the insight the model was built to provide in the first place.
A causal map or influence diagram illustrates the elements of a model and the fundamental relationships between them. Causality provides a natural language to discuss how interventions – decisions – propagate down causal chains to effect outcomes. A causal map represents mathematical relationships without mathematics, allowing stakeholders to take ownership of the scope of a model, the data that conditions it as well as the metrics that monitor and validate it, without getting tied down in notation.
