A Caltech Library Service

Causal Feature Learning: An Overview

Chalupka, Krzysztof and Eberhardt, Frederick and Perona, Pietro (2017) Causal Feature Learning: An Overview. Behaviormetrika, 44 (1). pp. 137-164. ISSN 0385-7417. doi:10.1007/s41237-016-0008-2.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


Causal feature learning (CFL) (Chalupka et al., Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. AUAI Press, Edinburgh, pp 181–190, 2015) is a causal inference framework rooted in the language of causal graphical models (Pearl J, Reasoning and inference. Cambridge University Press, Cambridge, 2009; Spirtes et al., Causation, Prediction, and Search. Massachusetts Institute of Technology, Massachusetts, 2000), and computational mechanics (Shalizi, PhD thesis, University of Wisconsin at Madison, 2001). CFL is aimed at discovering high-level causal relations from low-level data, and at reducing the experimental effort to understand confounding among the high-level variables. We first review the scientific motivation for CFL, then present a detailed introduction to the framework, laying out the definitions and algorithmic steps. A simple example illustrates the techniques involved in the learning steps and provides visual intuition. Finally, we discuss the limitations of the current framework and list a number of open problems.

Item Type:Article
Related URLs:
URLURL TypeDescription ReadCube access
Chalupka, Krzysztof0000-0002-1225-2112
Perona, Pietro0000-0002-7583-5809
Additional Information:© The Behaviormetric Society 2016. Received: 29 August 2016 / Accepted: 29 November 2016 / Published online: 26 December 2016. Communicated by Shohei Shimizu. We thank an anonymous reviewer for pointing out an error in our original theorem. This work was supported by NSF Award #1564330. On behalf of all authors, the corresponding author states that there is no conflict of interest.
Funding AgencyGrant Number
Subject Keywords:Causal discovery; Causal inference; Graphical models; Bayesian networks; Macrovariables; Multiscale modeling
Issue or Number:1
Record Number:CaltechAUTHORS:20170530-090152248
Persistent URL:
Official Citation:Chalupka, K., Eberhardt, F. & Perona, P. Behaviormetrika (2017) 44: 137. doi:10.1007/s41237-016-0008-2
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:77825
Deposited By: George Porter
Deposited On:30 May 2017 17:16
Last Modified:15 Nov 2021 17:34

Repository Staff Only: item control page