Tractable Uncertainty for Structure Learning

TL;DR

TRUST introduces a probabilistic circuit-based framework for Bayesian structure learning, enabling richer uncertainty modeling and more accurate structure inference compared to traditional sample-based methods.

Contribution

It presents a novel approach using probabilistic circuits for approximate posterior inference in structure learning, improving both structure quality and uncertainty estimation.

Findings

Probabilistic circuits capture a richer DAG space.

TRUST improves structure inference accuracy.

Enhanced uncertainty quantification in structure learning.

Abstract

Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate posterior inference that relies on probabilistic circuits as the representation of our posterior belief. In contrast to sample-based posterior approximations, our representation can capture a much richer space of DAGs, while also being able to tractably reason about the uncertainty through a range of useful inference queries. We empirically show how probabilistic circuits can be used as an augmented representation for structure learning methods, leading to improvement in both the quality of inferred structures and posterior uncertainty. Experimental results on conditional query answering further demonstrate the practical utility of the representational capacity of TRUST.