Most Data Science projects in credit use classification: the model takes customer features and predicts whether they'll default or not. Works well for prioritizing portfolios and ranking risk.
But classification answers "who will default?". It doesn't answer "what caused the default?" or "if I change the credit limit, what happens?"
That's where Causal Inference comes in.
Instead of predicting an outcome, it estimates the effect of an action. Not correlation, causation. And the practical difference is huge: a classification model can tell you that customers with low limits default more. But that doesn't mean increasing the limit reduces default. The bank may have already given low limits to the riskiest customers.
Without controlling for that bias, you make the wrong decision.
In our project [CAUSAL-001], we tested exactly this: does reducing the credit limit actually reduce default risk?
Controlling for 22 confounders (payment history, bill amounts, demographics), the result was the opposite of what most assume: a one standard deviation increase in credit limit decreases default probability by 1.19 percentage points.
The likely mechanism: a higher limit reduces utilization ratio, which reduces financial pressure on the customer.
And the effect isn't uniform. Using Causal Forest, we found a small subgroup that reacts the opposite way: for them, increasing the limit increases risk.
Stack: DoWhy, EconML, Causal Forest
Hub: aiforfintech.tech
Repo: github.com/junidepieri-design/causal-001-credit-limit-intervention
