3 papers
stat.AP2026
Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
Jose Benitez-Aurioles, Ricardo Silva, Brian McMillan +1
In routine care, individuals identified a priori as high-risk are usually tested for conditions more frequently. Protected attributes, such as sex or ethnicity may also determine t…
cs.LG2026
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…
stat.ME2025
Treatment effect extrapolation in the presence of unmeasured confounding
Stephanie Riley, Ricardo Silva, Matthew Sperrin
While randomised controlled trials (RCTs) are the gold standard for estimating causal treatment effects, their limited sample sizes and restrictive criteria make it difficult to ex…