Machine learning and microeconomic analysis

Question

When can a predictive model complement, rather than replace, the reasoning behind a microeconomic research question?

Beyond causal inference

Traditional economics has relied heavily on parametric models. Machine learning adds flexibility for discovering relationships, but it does not replace the conceptual work behind a good causal question.

Tree-based models and regularization can complement econometrics when the goal is prediction, variable selection, or the study of heterogeneous effects. Model choice should respond to the decision the analysis is meant to support, not only to a fit metric.

Limits

An accurate prediction does not identify a causal effect by itself. The proposal also requires separating training and evaluation data, explaining the target variable, and checking whether the model amplifies biases already present in the data.

The future of the data economist is not to abandon econometrics, but to extend it with tools that respect the context of each decision.