欧宝娱乐

Pearl explains why the theory and practice of statistics developed almost exclusively around correlation. Moreover, when big data and AI fields started to employ more and more statistics, the need for causal explanations turned this limitation into a big problem. For example, in order to show causality, statistics needs to add/employ concepts and practices like: randomization, intervention, control groups, prior specifications and limitations, hypotheses, prospective studies, adjustments, and so on. Pearl claims that his new theory of causality may bypass all of these limitations and difficulties; if a causal diagram can be provided along with the basic observational/correlation data.
In the end, it can be argued that causality is a metaphysical concept that cannot be discovered in the data; but only functions as a human category that directs the collection, organization, explanation, and so on of the data. It feels to me that Pearl is trying to go beyond this in order to provide a scientific foundation for causality as if it really exists in the world. His proposal in the last chapter to add free will, and thus a potential ability to make/assess counterfactual statements and to understand causality, to the current AI approaches seems not at all fundamental, practical, or significant to me.
In order to defend the introduction of causal diagrams and to defend their scientific status, Pearl stated in this book that: 鈥淟ogic void of representation is metaphysics鈥�. This struck me as a strange statement; since logic understood as representation is metaphysics.