Causal Feature Learning

16 Pages Posted: 29 Mar 2022

See all articles by Iman A. Wahle

Iman A. Wahle

California Institute of Technology

Jenna Kahn

California Institute of Technology

Frederick Eberhardt

California Institute of Technology

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

Suggested Citation

Wahle, Iman A. and Kahn, Jenna and Eberhardt, Frederick, Causal Feature Learning. Available at SSRN: https://ssrn.com/abstract=4066510 or http://dx.doi.org/10.2139/ssrn.4066510

Iman A. Wahle (Contact Author)

California Institute of Technology ( email )

Pasadena, CA 91125
United States

Jenna Kahn

California Institute of Technology ( email )

Pasadena, CA 91125
United States

Frederick Eberhardt

California Institute of Technology ( email )

Pasadena, CA 91125
United States

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