Causal Machine Learning

Sat, 04 Apr 2026 21:39:09 +0200

Modern machine learning is incredible at finding statistical patterns in large datasets. Neural networks regularly reach or beat human performance in vision, speech, and language tasks.

However, relying only on statistical correlations comes with a major catch: standard models assume that the test data follows the exact same distribution as the training data. In the real world, data distributions shift. When this occurs, purely statistical models often fail.

To build models that are robust to distribution shifts, we have to look beyond pure correlation and turn to causal inference.

Causality on Elias Wendt

Causal Machine Learning