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

• URL: http://arxiv.org/abs/2102.10440v2
• Date: Tue, 23 Feb 2021 19:53:20 GMT
• Title: Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
• Authors: Matej Ze\v{c}evi\'c, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan and Kristian Kersting
• Abstract summary: We consider the problem of learning interventional distributions using sum-product networks (SPNs) We provide an arbitrarily intervened causal graph as input, effectively subsuming Pearl's do-operator. The resulting interventional SPNs are motivated and illustrated by a structural causal model themed around personal health.
• Score: 26.497268758016595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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