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

Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting

Keywords: Interventions, Causality, Tractablity, Probabilistic Models

TL;DR: We consider the problem of learning interventional distributions (i.e., answering causal queries) with tractable probabilistic models (gated SPN).

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

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Code: https://github.com/zecevic-matej/iSPN

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/interventional-sum-product-networks-causal/code)