Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
·288 words
We introduce a causal discovery method that estimates uncertainty and refines results with expert feedback. Using generative flow networks, we sample belief-based ancestral graphs that captures latent-confounding, and iteratively reduce uncertainty through human input, with a human-in-the-loop approach.