Machine learning in policy evaluation: new tools for causal inference. - Noemi Kreif; Karla DiazOrdaz (CoRR, 2019)

On Locality of Local Explanation Models. - Sahra Ghalebikesabi; Lucile Ter-Minassian; Karla DiazOrdaz; Chris C. Holmes (NeurIPS, 2021)

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction. - Rose Sisk; Lijing Lin; Matthew Sperrin; Jessica K. Barrett; Brian D. M. Tom; Karla Diaz-Ordaz; Niels Peek; Glen P. Martin (J. Am. Medical Informatics Assoc., 2021)

PWSHAP: A Path-Wise Explanation Model for Targeted Variables. - Lucile Ter-Minassian; Oscar Clivio; Karla DiazOrdaz; Robin J. Evans 002; Christopher C. Holmes (ICML, 2023)

Explainable AI for survival analysis: a median-SHAP approach. - Lucile Ter-Minassian; Sahra Ghalebikesabi; Karla Diaz-Ordaz; Chris C. Holmes (CoRR, 2024)

Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data. - Matthew Pryce; Karla Diaz-Ordaz; Ruth H. Keogh; Stijn Vansteelandt (CoRR, 2024)

Investigating fair data acquisition for risk prediction in resource-constrained settings. - Ioanna Thoma; Elisabeth Abhayaratna; Matthew Sperrin; Karla Diaz-Ordaz; Ricardo Silva; Brieuc Lehmann (EWAF, 2025)