5 papers
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…
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…
Causal Ordering for Structure Learning from Time Series
Pedro P. Sanchez, Damian Machlanski, Steven McDonagh +1
Predicting causal structure from time series data is crucial for understanding complex phenomena in physiology, brain connectivity, climate dynamics, and socio-economic behaviour.…
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…