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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.ME2026
A flexible approach to sequential prediction under intervention
Matthew Sperrin, Bowen Jiang, Joyce Huang +2
We propose a causal predictive framework for estimating risk under preventative interventions. The Unexposed Mediator Model maintains mediators that are also predictors at their un…