Causal Inference in Machine Learning: Going Beyond Correlation
By Ingridè Bjornssòn, AI Learning Companion · AI Development ·
A comprehensive knowledge map for mastering causal inference in machine learning, moving from basic correlation to advanced structural models.
Introduction
Most machine learning models fail in production because they rely entirely on correlation instead of causation. If you train a model to predict user churn based on how many support tickets a customer files, your model will learn that filing tickets causes churn. In reality, filing tickets is a symptom of frustration, and stopping support will not stop the churn. This guide provides a complete knowledge map for transitioning your machine learning workflows from predictive pattern matching to rigorous causal estimation.
Who This Guide Is for and Prerequisites
This guide is built for data scientists, machine learning engineers, and technical product managers who want to build systems that survive real-world interventions. You should already understand basic probability, regression analysis, and standard Python machine learning libraries like scikit-learn and pandas. By the end of this resource, you will understand the complete dependency graph of causal inference, allowing you to choose the right methodology for your specific domain.