Causal Learning without DAGs

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Authors David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt
Journal/Conference Name Neural Information Processing Systems
Paper Category
Paper 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.
Date of publication 2008
Code Programming Language MATLAB

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