The question comes up in every data project: should we use a classical econometric regression or a machine learning model? The answer is not 'ML is always better'. Here is our decision framework based on years of enterprise projects.
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.
With care,
Excellent article, this matches exactly what we're seeing with our enterprise clients. The section on inference costs is especially valuable. It's a topic most articles gloss over but it's make-or-break at scale.
Thanks James! Inference cost optimization is often deprioritized during prototyping but becomes critical in production. Feel free to book a session if you'd like to go deeper on this.
Sharing this with my whole team. The distinction between an impressive demo and robust production is exactly the debate we're having internally right now. The human checkpoint advice is immediately actionable.
Great article. I'd push back slightly on the 18-day deployment estimate, in our experience with enterprise security and GDPR requirements, 4–6 weeks is more realistic for a first production agent.
Completely fair point David. The 18 days refers to a scoped first agent in a test environment. For full enterprise production with security constraints, your estimate is accurate.