The formula is not a clerical detail A business asks a causal question: What happens if we change X? The analytics team returns a formula. Everyone relaxes. The effect is identifiable, the notation looks official, and a graph somewhere has probably been blessed by someone with a PhD. Excellent. Time to move to dashboards. ...
TL;DR for operators Most business causal analysis begins with an uncomfortable little fiction: that someone knows the causal graph. The marketing team wants to know whether a campaign caused retention. The risk team wants to know whether a policy change reduced defaults. The operations team wants to know whether a staffing rule improved service levels. Everyone has observational data. Nobody has a clean experimental intervention. Somewhere, usually in a deck with too many arrows, a causal diagram appears. ...