How to read these
Each case opens with what the standard analysis was saying and walks through how the causal model separated the program’s effect from the selection driving it. The model file ships with every case.
The cases
Bank Churn → The program that looked like it worked.
£2.4M spent on a retention campaign. The data showed it working. The causal model asked the right question: would those customers have stayed regardless?
Credit Risk → We measured program effectiveness by looking at enrolled borrowers.
Borrowers selected into the program based on intent to stay. The observed effect is mostly the selection, not the program. The standard analysis confounded the two; the causal model separates them.
M&A Due Diligence → The team that resisted the retention clause is the most capable team.
The negotiating signal is informative about exactly the variable the acquirer wants to retain. A causal model represents how the diligence process itself selects on the outcome it is trying to measure.
Rent vs Buy → A rate rise makes buying more expensive. It also cools prices.
The two effects move in opposite directions for the buyer. A spreadsheet that treats rates and prices as independent inputs answers a different question than the one the decision requires.
Next
For the methods behind these cases, see Confounding (why selection breaks naive comparisons) and Querying (how a built model is interrogated). For the wider portfolio, see Cases.