4 papers · 1 filter
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…
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.…
SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigation
Junyu Yan, Feng Chen, Yuyang Xue +4
Recent studies have shown that Machine Learning (ML) models can exhibit bias in real-world scenarios, posing significant challenges in ethically sensitive domains such as healthcar…
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…