Data Science #31 - Correlation and causation (1921), Wright Sewall

On the 31st episode of the podcast, we add Liron to the team, we review a gem from 1921, where Sewall Wright introduced path analysis, mapping hypothesized causal arrows into simple diagrams and proving that any sample correlation can be written as the sum of products of “path coefficients.”

By treating each arrow as a standardised regression weight, he showed how to split the variance of an outcome into direct, indirect, and joint pieces, then solve for unknown paths from an ordinary correlation matrix—turning the slogan “correlation ≠ causation” into a workable calculus for observational…