Causal learning without DAGs
David Duvenaud, Daniel Eaton, Kevin Murphy, and Mark Schmidt; JMLR W&CP 6:177-190, 2010.
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.