Accuracy is comforting because it gives us a number. The model predicted the right label. The chatbot chose the same option as the survey respondent. The simulated customer picked the same product. Everyone claps, someone updates a dashboard, and the alignment problem is declared mostly solved. Unfortunately, decision-making is where accuracy goes to look respectable while quietly doing very little. ...
Drilling is where optimism receives an invoice. In mineral exploration, maps can look promising, models can look elegant, and geophysical anomalies can glow like destiny on a consultant’s slide deck. Then the drill rig arrives. A few expensive holes later, the anomaly turns out not to be an economic mineral system, the team moves to the next target, and everyone quietly files the failed interpretation under “learning.” Very scientific. Very costly. ...
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. ...