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This paper introduces a Bayesian approach to conditional causal discovery, where the posterior over causal graphs and parameters is conditioned on user-specified causal-effect constraints (e.g., a large causal effect). They adapt rare-event estimation techniques to handle events with small posterior mass and validate the method on synthetic data and the Sachs protein dataset.
This paper presents QANTIS, a method that uses IBM Heron quantum hardware to perform calibrated belief updates for Partially Observable Markov Decision Processes (POMDPs), focusing on reusing a quantum belief-update primitive across sequential decision steps without corrupting the posterior. The authors validate the approach with a controlled case study on the Tiger POMDP, showing that the hardware posterior selects the same actions as exact Bayesian inference.