Related Posts

Share This

Causality Modeling

Causality is the relationship between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first.

Please find below the basic causality models:


In a causal model, circular rules are not allowed. If that rule were to be broken, the cause and effect definition would not make sense.

While creating a causal model, you might fall into another trap: if A and B are statistically correlated, it does not necessarily imply that one implies the other. The causal model could a common cause model, such as C implies A; C implies B but A and B not causally related.

Performance and results indicators are linked together in a causal manner. In order to complete the organization synthesis, you might want to draw a natural relationship between them. The relationship hypothesis may be later verified with statistical tests.

When drawing connecting arrows between the indicators representing the relationships, you might want to had a minus or plus sign to distinguish the cases if the indicators are proportional or inverse of each other’s.