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cs.LG2026
A Causal Foundation Model for Structure and Outcome Prediction
Max Zhu, Martino Mansoldo, Ching-Hao Wang +1
We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, support…
cs.LG2022★ 2 cited
SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction
Intae Moon, Stefan Groha, Alexander Gusev
Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss t…