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We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network on a graph with n discrete variables and bounded in-degree and bounded `confounded components', we show that O(logn) interventions on an unknown causal Bayesian network on the same graph, and Õ (n/ϵ2) samples per intervention, suffice to efficiently distinguish whether = or whether there exists some intervention under which and are farther than ϵ 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: Ω(logn) 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.
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Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing.
We show that the square Hellinger distance between two Bayesian networks on the same directed graph, G, is subadditive with respect to the neighborhoods of G. Namely, if P and Q are the probability distributions defined by two Bayesian networks on the same DAG, our inequality states that the square Hellinger distance, H2(P,Q), between P and Q is upper bounded by the sum, ∑vH2(P{v}∪Πv,Q{v}∪Πv), of the square Hellinger distances between the marginals of P and Q on every node v and its parents Πv in the DAG. Importantly, our bound does not involve the conditionals but the marginals of P and Q. We derive a similar inequality for more general Markov Random Fields. As an application of our inequality, we show that distinguishing whether two Bayesian networks P and Q on the same (but potentially unknown) DAG satisfy P=Q vs dTV(P,Q)>ϵ can be performed from Õ (|Σ|3/4(d+1)⋅n/ϵ2) samples, where d is the maximum in-degree of the DAG and Σ the domain of each variable of the Bayesian networks. If P and Q are defined on potentially different and potentially unknown trees, the sample complexity becomes Õ (|Σ|4.5n/ϵ2), whose dependence on n,ϵ is optimal up to logarithmic factors. Lastly, if P and Q are product distributions over {0,1}n and Q is known, the sample complexity becomes O(n‾√/ϵ2), which is optimal up to constant factors.
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- Award ID(s):
- 1650733
- PAR ID:
- 10086311
- Date Published:
- Journal Name:
- 30th Annual Conference on Learning Theory
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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