Causal learning without DAGs

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.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: David Duvenaud, Daniel Eaton, Kevin P. Murphy, Mark W. Schmidt
• Keywords: Causal Learning Methods, Causal Prediction Tasks, DAG-based Methods, JMLR 2010