Role of regression in causality

Let’s start with a bit of background on regression. A historical understanding of regression may enrich your appreciation for the methodological nuances and widespread application in contemporary causal research. The foundation of regression analysis can be traced back to Sir Francis Galton, who, in the late 19th century, introduced the concept of regression . Galton’s work was initially concerned with understanding hereditary characteristics, but his developed principles laid the groundwork for statistical regression [1]. Karl Pearson and Yule further developed regression analysis in the early 20th century, focusing on correlation and linear regression models. Their work emphasized the mathematical relationships between variables, setting the stage for the later use of regression in causal analysis [2,3].

A significant milestone in using regression methods for causal inference was the development of the Rubin Causal Model (RCM) [4]...