Causal Feature Learning

Abstract

Causal Feature Learning (CFL) is a domain-general framework that learns macro-level causes and effects from micro-level data. In contrast to traditional coarsening methods, CFL bases its aggregation on the relation between two micro-variables, providing macro-level representations of each variable with respect to it’s relationship with the other. Here, we implement a CFL software package that aims to reduce the barriers to running CFL. With customizable models, thorough documentation, and helper methods to interpret results, CFL makes it possible for researchers to easily analyze and identify potentially relevant features in their data.

Keywords: Macrovariables, Aggregation, Coarsening of Relations, Causal Representation Learning