[2011.02677] The causal foundations of applied probability and statistics

Statistical science (as opposed to mathematical statistics) involves far more than probability theory, for it requires realistic causal models of data generators - even for purely descriptive goals. Statistical decision theory requires more causality: Rational decisions are actions taken to minimize costs while maximizing benefits, and thus require explication of causes of loss and gain. Competent statistical practice thus integrates logic, context, and probability into scientific inference and decision using narratives filled with causality. This reality was seen and accounted for intuitively by the founders of modern statistics, but was not well recognized in the ensuing statistical theory (which focused instead on the causally inert properties of probability measures). Nonetheless, both statistical foundations and basic statistics can and should be taught using formal causal models. The causal view of statistical science fits within a broader information-processing framework which illuminates and unifies frequentist, Bayesian, and related probability-based foundations of statistics. Causality theory can thus be seen as a key component connecting computation to contextual information, not extra-statistical but instead essential for sound statistical training and applications.

[2011.02677] The causal foundations of applied probability and statistics Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Statistics > Other Statistics arXiv:2011.02677 (stat) [Submitted on 5 Nov 2020 ( v1 ), last revised 1 Jun 2022 (this version, v5)] Title: The causal foundations of applied probability and statistics Authors: Sander Greenland View a PDF of the paper titled The causal foundations of applied probability and statistics, by Sander Greenland View PDF Abstract: Statistical science (as op

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