The foundational texts on statistical causation frequently remind us that causal inference is necessary to estimate the outcomes of decisions. Suppose for instance that we have access to a trove of medical reports describing the outcomes $Z$ of applying medical treatment $Y$ to patients presenting symptom $X$. It is well known that the conditional distribution $P(Z|X,Y)$ estimated using such a dataset is a poor way to determine which treatment works best. More precisely, this approach fails badly when there exist so called confounding variables $U$ that
Although causation is a crucial component of the human cognitive experience, giving a precise and complete definition of causation has proven surprisingly challenging. The purpose of this page is to outline three very different viewpoints on causation that I believe relevant for Artificial Intelligence and inadequately addressed by Machine Learning techniques.