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stat.ML2026
Interventional Processes for Causal Uncertainty Quantification
Hugh Dance, Peter Orbanz, Arthur Gretton
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 es…
stat.ML2026
Debiased Counterfactual Generation via Flow Matching from Observations
Hugh Dance, Johnny Xi, Peter Orbanz +1
Estimating counterfactual distributions under interventions is central to treatment risk assessment and counterfactual generation tasks. Existing approaches model the counterfactua…
stat.ML2025
Distinguishing Cause from Effect with Causal Velocity Models
Johnny Xi, Hugh Dance, Peter Orbanz +1
Bivariate structural causal models (SCM) are often used to infer causal direction by examining their goodness-of-fit under restricted model classes. In this paper, we describe a pa…