What is Causal Process Mining?

Causal Process Mining is an approach that goes beyond the temporal order of activities to uncover the actual cause-and-effect relationships between them. Instead of inferring connections from mere sequence, it models which events genuinely trigger which others.

Why does this matter?

Classic approaches often confuse temporal proximity with causality, producing misleading process models. Causal Process Mining delivers correct relationships – essential for reliable root-cause analysis, sound decisions, and the meaningful use of AI.

What does this look like in practice?

Classic process mining shows that a credit check is frequently followed by rework. That is a frequency, not a cause. Causal process mining tests whether the rework actually follows from the credit check, or whether both depend on a third factor — incomplete master data, say. Only that distinction tells you where an intervention can work at all.

Not to be confused with

Not to be confused with directly-follows analysis, which counts which step follows which. Succession is not causation: two steps can occur in sequence regularly without one triggering the other. Closing that gap is precisely what the causal approach is for.