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.
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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.