AI for economics covers how economists use machine learning in forecasting and research without giving up causal credibility or reproducibility.
Predictive lift is easy to claim. A model can forecast better on paper and still be wrong if its identification strategy breaks under a policy shift, and regulators will ask about exactly that. Business decisions carry the same risk: a pricing or expansion call built on an economic forecast inherits every assumption the forecast hid.
These articles cover AI methods and workflows for reproducible economic research, and modern workflows for decision makers who need forecasts that survive structural breaks and outside scrutiny. Start with AI for economics: methods, workflows and reproducible research.
In Suprmind, five frontier models work on the same question in one conversation and see each other’s answers, so a hidden assumption has more than one chance to be caught. See AI for researchers.
