The causal foundations of applied probability and statistics

TL;DR

This paper argues that realistic causal models are essential for statistical science, decision-making, and teaching, integrating causality with probability to unify various statistical foundations.

Contribution

It emphasizes the importance of formal causal models in statistical theory, practice, and education, bridging the gap between probability and causality.

Findings

Causality is fundamental to statistical decision-making.

Statistical foundations should incorporate causal models.

A broader information-processing framework unifies different statistical paradigms.

Abstract

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.