3 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
Efficiently Vectorized MCMC on Modern Accelerators
Hugh Dance, Pierre Glaser, Peter Orbanz +1
With the advent of automatic vectorization tools (e.g., JAX's ), writing multi-chain MCMC algorithms is often now as simple as invoking those tools on single-chain c…
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…
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…
Counterfactual Cocycles: A Framework for Robust and Coherent Counterfactual Transports
Hugh Dance, Benjamin Bloem-Reddy
Estimating joint distributions (a.k.a. couplings) over counterfactual outcomes is central to personalized decision-making and treatment risk assessment. Two emergent frameworks wit…
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
Hugh Dance, Brooks Paige
Variable selection in Gaussian processes (GPs) is typically undertaken by thresholding the inverse lengthscales of automatic relevance determination kernels, but in high-dimensiona…