Controlling for Biasing Signals in Images for Prognostic Models: Survival Predictions for Lung Cancer with Deep Learning. - Wouter A. C. van Amsterdam; Marinus J. C. Eijkemans (CoRR, 2019)

Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning. - Wouter A. C. van Amsterdam; Joost J. C. Verhoeff; Pim A. de Jong; Tim Leiner; Marinus J. C. Eijkemans (npj Digit. Medicine, 2019)

Decision making in cancer: Causal questions require causal answers. - Wouter A. C. van Amsterdam; Pim A. de Jong; Joost J. C. Verhoeff; Tim Leiner; Rajesh Ranganath (CoRR, 2022)

A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions. - Wouter A. C. van Amsterdam (CoRR, 2024)

From algorithms to action: improving patient care requires causality. - Wouter A. C. van Amsterdam; Pim A. de Jong; Joost J. C. Verhoeff; Tim Leiner; Rajesh Ranganath (BMC Medical Informatics Decis. Mak., 2024)

Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do. - Yoav Wald; Mark Goldstein; Yonathan Efroni; Wouter A. C. van Amsterdam; Rajesh Ranganath (ICLR, 2025)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - Shalmali Joshi; Iñigo Urteaga; Wouter A. C. van Amsterdam; George Hripcsak; Pierre A. Elias; Benjamin R. C. Amor; Noémie Elhadad; James C. Fackler; Mark P. Sendak; Jenna Wiens; Kaivalya Deshpande; Yoav Wald; et al. (J. Am. Medical Informatics Assoc., 2025)

When accurate prediction models yield harmful self-fulfilling prophecies. - Wouter A. C. van Amsterdam; Nan van Geloven; Jesse H. Krijthe; Rajesh Ranganath; Giovanni Cinà (Patterns, 2025)