arXiv Machine Learning June 2, 2026 By Hugh Dance, Peter Orbanz, Arthur Gretton

Interventional Processes for Causal Uncertainty Quantification

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arXiv:2410. 14483v3 Announce Type: replace-cross Abstract: Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar estimand.

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Instrumental and Proximal Causal Inference with Gaussian Processes

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