AI RESEARCH
Interventional Processes for Causal Uncertainty Quantification
arXiv CS.LG
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ArXi:2410.14483v3 Announce Type: replace-cross 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. In this work, we