5 citations · 7 across the 9 of their papers we have counts for
12 papers
Causal Spatio-Temporal Neural Distance Fields for Counterfactual Cardiac Anatomy
Mathias Lowes, Kurt Butler, Kristine Aavild Sørensen +4
Counterfactual cardiac anatomy can support risk modelling and intervention planning, by predicting how a patient's heart would change under altered clinical conditions. However, cu…
Large Language Models Extend Known Research But Do Not Create New Research Directions
Zhihua Liu, Kurt Butler, Damian Machlanski +2
The success of autonomous research systems relies on the quality of the scientific hypotheses they generate. Recent studies report that human reviewers often rate LLM-generated ide…
Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
Junyu Yan, Damian Machlanski, Kurt Butler +4
Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to design optimally by hand when te…
A Causal Framework for Mitigating Data Shifts in Healthcare
Kurt Butler, Stephanie Riley, Damian Machlanski +13
Developing predictive models that perform reliably across diverse patient populations and heterogeneous environments is a core aim of medical research. However, generalization is o…
Rethinking Chronological Causal Discovery with Signal Processing
Kurt Butler, Damian Machlanski, Panagiotis Dimitrakopoulos +1
Causal discovery problems use a set of observations to deduce causality between variables in the real world, typically to answer questions about biological or physical systems. The…
Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Kurt Butler, Guanchao Feng, Petar Djuric
Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation…
A Shift in Perspective on Causality in Domain Generalization
Damian Machlanski, Stephanie Riley, Edward Moroshko +7
The promise that causal modelling can lead to robust AI generalization has been challenged in recent work on domain generalization (DG) benchmarks. We revisit the claims of the cau…
Trustworthy Prediction with Gaussian Process Knowledge Scores
Kurt Butler, Guanchao Feng, Tong Chen +1
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are we…
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
Kurt Butler, Daniel Waxman, Petar M. Djurić
Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have…
On Counterfactual Interventions in Vector Autoregressive Models
Kurt Butler, Marija Iloska, Petar M. Djuric
Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without estab…
Explainable Learning with Gaussian Processes
Kurt Butler, Guanchao Feng, Petar M. Djuric
The field of explainable artificial intelligence (XAI) attempts to develop methods that provide insight into how complicated machine learning methods make predictions. Many methods…
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
Daniel Waxman, Kurt Butler, Petar M. Djuric
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery u…