Causal Modelling: The Two Cultures

Abstract

We offer descriptive and normative standards for the principled pursuit of causal inference. These standards address critiques of both the algorithmic and the data modeling cultures identified in (Breiman, 2001), and provide a fruitful synthesis of both cultures. We contrast the resulting ”cautious causal inference” with overly optimistic methods inspired by algorithmic data analysis methods prevalent in machine learning, as well as older approaches to causal modeling that employ overly restrictive parametric models.