Why this question matters
In practice, choosing the wrong model type is costly. A complex ML model on a 500-row dataset will be unstable. A linear regression on a problem with 50 non-linear variables will be biased.
When econometrics wins
You must explain and defend your results
In finance, healthcare, legal: you must explain why the model makes a given decision. A logistic regression with interpretable coefficients is infinitely more defensible before a regulator than a gradient boosting with 500 trees.
You have little data
With fewer than 1,000 observations, ML models tend to overfit. A well-specified econometric model will give better out-of-sample results.
You are doing causal inference
If you want to answer 'what is the effect of a 10% price increase on sales?', that is a causal question. Econometrics, with instrumental variables and difference-in-differences, is designed for that. ML predicts, it does not explain causality.
When ML wins
Massive unstructured data: text, images, web behaviours. Highly non-linear patterns. Pure prediction where interpretability is not critical.
Our hybrid approach: we often start with an econometric model to understand key relationships, then move to ML to improve predictive accuracy. Both are complementary, not rivals.
The econometrics and statistics references cited here are open access in our scientific library, each with its own summary note.
