PIRSA:24050094

Quantum Foundations SeminarBayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

Nishikawa-Toomey, M. (2024). Quantum Foundations SeminarBayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes. Perimeter Institute. https://pirsa.org/24050094

Nishikawa-Toomey, Mizu. Quantum Foundations SeminarBayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes. Perimeter Institute, May. 28, 2024, https://pirsa.org/24050094 

          @misc{ pirsa_PIRSA:24050094,
            doi = {10.48660/24050094},
            url = {https://pirsa.org/24050094},
            author = {Nishikawa-Toomey, Mizu},
            keywords = {Quantum Foundations},
            language = {en},
            title = {Quantum Foundations SeminarBayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes},
            publisher = {Perimeter Institute},
            year = {2024},
            month = {may},
            note = {PIRSA:24050094 see, \url{https://pirsa.org}}
          }

Mizu Nishikawa-Toomey

Mila - Quebec Artificial Intelligence Institute

Talk number
PIRSA:24050094
Collection
Talk Type
Subject
Abstract

Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that define the relationship between parent and child variables. By taking a Bayesian approach, it is possible to reason about the uncertainty of the causal model. The notion of modelling the uncertainty over models is particularly crucial for causal structure learning since the model could be unidentifiable when given only a finite amount of observational data. In this paper, we introduce a novel method to jointly learn the structure and mechanisms of the causal model using Variational Bayes, which we call Variational Bayes-DAG-GFlowNet (VBG). We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model. Our results on simulated data suggest that VBG is competitive against several baselines in modelling the posterior over DAGs and mechanisms, while offering several advantages over existing methods, including the guarantee to sample acyclic graphs, and the flexibility to generalize to non-linear causal mechanisms.

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