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We consider the problem of efficiently inferring interventional distributions in a causal Bayesian network from a finite number of observations.
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Learning and sampling of atomic interventions from observations
A. Bhattacharyya, S. Gayen, S. Kandasamy, A. Maran, and V. N. Variyam
Cited in the paper.
Efficient distance approximation for structured high-dimensional distributions via learning
A. Bhattacharyya, S. Gayen, K. S. Meel, and N. Vinodchandran
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