[D] Why Causality Matters for Production ML: Moving Beyond Correlation

After 8 years building production ML systems (in data quality, entity resolution, diagnostics), I keep running into the same problem:

Models with great offline metrics fail in production because they learn correlations, not causal mechanisms.

I just started a 5-part series on building causal ML systems on the NeoForge Labs research blog. Part 1 covers:

1. Why correlation fails – The ice cream/drowning example, but with real production failures
2. Pearl’s Ladder of Causation – Association, Intervention, Counterfactuals
3. Practical implications – When does this actually matter?
4. Case study – Plant disease diagnosis (correlation vs. causal approach)

Key insight: Your model can predict disease with 90% accuracy but still give recommendations that make things worse. Because prediction ≠ intervention.

The series builds up to implementing a full causal inference system using DoWhy, with counterfactual reasoning and intervention optimization.

Link (free to read): https://blog.neoforgelabs.tech/why-causality-matters-for-ai

(Also available on Medium for members)

Next parts:

– Part 2 (Wed): Building Causal DAGs

– Part 3 (Fri): Counterfactual Reasoning

– Parts 4-5 (next week): Interventions + Distributed Systems

Would love to hear your thoughts, especially if you’ve dealt with distribution shift, confounding, or intervention prediction in production.

Questions I’m exploring:

– When is causal inference overkill vs. essential?

– What’s the practical overhead of DAG construction?

– How do you validate causal assumptions?

Happy to discuss in the comments!