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Learning and Testing Causal Models with Interventions

Acharya, Jayadev and Bhattacharyya, Arnab and Daskalakis, Constantinos and Kandasamy, Saravanan (2018) Learning and Testing Causal Models with Interventions. In: 32nd Conference on Neural Information Processing Systems (NIPS), December 2 - 8, 2018, Montreal, Canada.

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Official URL: https://papers.nips.cc/paper/8155-learning-and-tes...

Abstract

We consider testing and learning problems on causal Bayesian networks as defined by Pearl Pea09]. Given a causal Bayesian network M on a graph with n discrete variables and bounded in-degree and bounded ``confounded components'', we show that O(log n) interventions on an unknown causal Bayesian network X on the same graph, and O(n/epsilon(2)) samples per intervention, suffice to efficiently distinguish whether X = M or whether there exists some intervention under which X and M are farther than epsilon in total variation distance. We also obtain sample/time/intervention efficient algorithms for: (i) testing the identity of two unknown causal Bayesian networks on the same graph; and (ii) learning a causal Bayesian network on a given graph. Although our algorithms are non-adaptive, we show that adaptivity does not help in general: Omega(log n) interventions are necessary for testing the identity of two unknown causal Bayesian networks on the same graph, even adaptively. Our algorithms are enabled by a new subadditivity inequality for the squared Hellinger distance between two causal Bayesian networks.

Item Type: Conference Proceedings
Additional Information: Copyright belongs to NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)
Keywords: Distributions;Selection;Circuits
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 21 Apr 2019 08:06
Last Modified: 21 Apr 2019 08:06
URI: http://eprints.iisc.ac.in/id/eprint/62308

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