Regularity Theory of Causation

Attributed to David Hume (A Treatise of Human Nature, 1739): there is nothing more to causation than a constant conjunction of two events — the regular, repeated succession of one kind of event by another. Causal necessity is not observable; only regularity is (sources/2023-anjum-rocca-phi403-lecture-04-whats-in-a-correlation).

Core commitments

Causation is universal — a covering-law model: C causes E iff all instances of C are followed by E (sources/2023-anjum-rocca-phi403-lecture-11-is-more-data-better).
Causation is observable only via its regularities — epistemological parsimony.
Same cause, same effect — "like causes always produce like effects" (Hume 1739) (sources/2023-anjum-rocca-phi403-lecture-05-same-cause-same-effect).
Implies: more data is better; representative sampling; statistical methods are the natural scientific method.

Strengths

Fits the empiricist ideal: only observable features enter the analysis.
Motivates correlation-driven science: statistics, RCTs, large-N studies.
Easy to operationalise.

Problems the course raises

Correlation is not causation — 5 possible conclusions from any correlation (C→E, E→C, common cause, mutual causation, spurious); spurious correlations (Tyler Vigen's divorce in Maine × margarine consumption in US) exhibit near-perfect regularities (sources/2023-anjum-rocca-phi403-lecture-04-whats-in-a-correlation).
Real causal connections are rarely perfect regularities — exceptions, outliers, noise, non-responders, interferers (sources/2023-anjum-rocca-phi403-lecture-05-same-cause-same-effect).
Additive interference breaks constant conjunction: A can cause B while an intervention I prevents B from following A (sources/2023-anjum-rocca-phi403-lecture-08-have-your-cause-and-beat-it).
The problem of induction (Hume's own): any finite data set underdetermines the regularity.
Modern statisticians work hard to separate genuine from accidental regularities — which implies they already take causation to be more than mere regularity.

Modern remnants

The theory is "alive and well" in the evidence hierarchy of evidence-based medicine, in the "correlations first" norm of quantitative science, and in the assumption that more data yields better causal claims. The course treats this as a philosophical bias scientists adopt implicitly.

concepts/causation · concepts/probabilistic-causation · concepts/dispositionalism (the main rival) · concepts/rct-limitations · concepts/philosophical-bias