DiBS: Differentiable Bayesian Structure Learning
What this work does
Introduces a latent differentiable distribution over DAGs and performs approximate joint graph/parameter posterior inference with particle-based variational methods.
Connection to discovery
Supplies an uncertainty-bearing belief model for selecting interventions rather than collapsing immediately to one guessed causal graph.
Evidence
Inspected gradient estimators and graph/predictive posterior evaluation, including expected SHD over equivalence classes.
Scope and limitations
Approximate posteriors depend on graph priors, likelihood assumptions and inference quality. Observational non-identifiability is not removed by differentiability.
THE RESEARCH CONVERSATION
Discussion & reactions
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