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

Matej Zečević · Devendra Dhami · Athresh Karanam · Sriraam Natarajan · Kristian Kersting

Keywords: [ Deep Learning ] [ Graph Learning ] [ Generative Model ] [ Causality ]


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 against competing methods from both generative and causal modelling demonstrates that interventional SPNs indeed are both expressive and causally adequate.

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