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Why Machine Learning Is Not Made for Causal Estimation
Predictive vs. causal inference: a critical distinction
Author: Quentin Gallea, PhD
July 18, 2024
Summary
Machine Learning is made essentially for predictive inference, which is inherently different from Causal Inference. Predictive models are incredibly powerful tools, allowing us to detect patterns and associations, but they fall short in explaining why events occur. This is where causal inference steps in, allowing for more informed decision-making, that can effectively influence outcomes and go beyond mere association.