Thinking by Professor Dan Franks
Most business decisions involve many levers. Most causal models get all but one of them wrong
Good business decisions don't mean looking at each lever in isolation. They mean pulling several together, and that is where standard causal machine learning falls apart.
Why a highly accurate model can still make terrible decisions
Two models. One predicts sales almost perfectly, yet gives disastrous business advice. The other is a terrible forecaster, yet gets the decision exactly right.
The big data fallacy
The bigger the dataset, the more likely we are to mistake correlation for causation. The good news is that causal thinking can be learned.
The simple task your AI can't learn
Your sales model predicts almost to the dollar. It has no idea what happens if you cut the ad budget.