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Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

Matej Zecevic; Devendra Singh Dhami; Athresh Karanam; Sriraam Natarajan; Kristian Kersting
In: Marc'Aurelio Ranzato; Alina Beygelzimer; Yann N. Dauphin; Percy Liang; Jennifer Wortman Vaughan (Hrsg.). Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021. Neural Information Processing Systems (NeurIPS-2021), December 6-14, Pages 15019-15031, Curran Associates, Inc. 2021.


While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference. As a step towards tractable causal models, we consider the problem of learning interventional distributions using sum-product networks (SPNs) that are over-parameterized by gate functions, e.g., neural networks. Providing an arbitrarily intervened causal graph as input, effectively subsuming Pearl's do-operator, the gate function predicts the parameters of the SPN. The resulting interventional SPNs are motivated and illustrated by a structural causal model themed around personal health. Our empirical evaluation on three benchmark data sets as well as a synthetic health data set clearly demonstrates that interventional SPNs indeed are both expressive in modelling and flexible in adapting to the interventions.

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