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arXiv · 2010.00684

Towards Scalable Bayesian Learning of Causal DAGs

Jussi Viinikka·Antti Hyttinen·Johan Pensar·Mikko Koivisto
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Abstract

We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov chain Monte Carlo scheme for learning Bayesian networks, which enables efficient approximate sampling from the graph posterior, provided that each node is assigned a small number $K$ of candidate parents. We present algorithmic techniques to significantly reduce the space and time requirements, which make the use of substantially larger values of $K$ feasible. Furthermore, we investigate the problem of selecting the candidate parents per node so as to maximize the covered posterior mass. Finally, we combine our sampling method with a novel Bayesian approach for estimating causal effects in linear Gaussian DAG models. Numerical experiments demonstrate the performance of our methods in detecting ancestor-descendant relations, and in causal effect estimation our Bayesian method is shown to outperform previous approaches.

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Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto. 2020-09-30. Towards Scalable Bayesian Learning of Causal DAGs. https://arxiv.org/abs/2010.00684

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