Rare-Event Estimation Enhances Bayesian Causal Discovery
A new arXiv preprint (2608.12640) introduces a method for interpretable causal discovery that incorporates causal-effect constraints into Bayesian inference. The approach targets the posterior distribution over causal graphs and parameters conditional on specific events, such as a large causal effect, addressing a computational challenge in existing Bayesian causal discovery methods. The authors adapt rare-event estimation techniques to efficiently sample from the joint graph-parameter space, gradually driving a particle population toward the constrained region. This allows for the interpretation and explanation of observed or hypothesized phenomena, moving beyond mere edge prediction. The method is particularly useful when the event of interest has small posterior mass, a scenario where traditional approaches struggle. The paper is categorized as a cross-type announcement and is available on arXiv.
Key facts
- The paper is titled 'Interpretable Causal Discovery via Causal-Effect Constraints'.
- It is available on arXiv with identifier 2608.12640.
- The announcement type is 'cross'.
- The method casts conditional causal discovery as a Bayesian inference problem.
- It targets the posterior over causal graphs and parameters conditional on an event.
- The approach adapts rare-event estimation techniques.
- It gradually drives a particle population toward the constrained region.
- The method maintains samples that approximate the conditional posterior.
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