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arXiv · 2102.10440

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

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

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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Matej Zečević, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting. 2021-02-20. Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. https://arxiv.org/abs/2102.10440

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