PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Causal Learning without DAGs
David Duvenaud, Daniel Daniel Eaton, Kevin Murphy and Mark Schmidt
Journal of Machine Learning Research Volume Challenges in Causality, Number W&CP 6, pp. 177-190, 2010.

Abstract

Causal learning methods are often evaluated in terms of their ability to discover a true underlying directed acyclic graph (DAG) structure. However, in general the true structure is unknown and may not be a DAG structure. We therefore consider evaluating causal learning methods in terms of predicting the effects of interventions on unseen test data. Given this task, we show that there exist a variety of approaches to modeling causality, generalizing DAG-based methods. Our experiments on synthetic and biological data indicate that some non-DAG models perform as well or better than DAG-based methods at causal prediction tasks.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7473
Deposited By:David Duvenaud
Deposited On:17 March 2011