Sampling causal structure hypotheses from a feature-attribution-derived distribution, instead of committing to a single causal graph, makes learned robot dynamics models more robust to noise and change at a fraction of the compute.
Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
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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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Learning Causal Structure Distributions for Robust Planning
Sampling causal structure hypotheses from a feature-attribution-derived distribution, instead of committing to a single causal graph, makes learned robot dynamics models more robust to noise and change at a fraction of the compute.