Selection, Ignorability and Challenges With Causal Fairness. - Jake Fawkes; Robin J. Evans 002; Dino Sejdinovic (CLeaR, 2022)

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap. - Jake Fawkes; Robert Hu; Robin J. Evans 002; Dino Sejdinovic (CoRR, 2022)

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. - Shahine Bouabid; Jake Fawkes; Dino Sejdinovic (ICML, 2023)

Results on Counterfactual Invariance. - Jake Fawkes; Robin J. Evans 002 (CoRR, 2023)

Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach. - Robert Grout; Rishab Gupta; Ruby Bryant; Mawada A. Elmahgoub; Yijie Li; Khushbakht Irfanullah; Rahul F. Patel; Jake Fawkes; Catherine Inness (Frontiers Artif. Intell., 2023)

The Role of Learning Algorithms in Collective Action. - Omri Ben-Dov; Jake Fawkes; Samira Samadi; Amartya Sanyal (ICML, 2024)

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning. - Jake Fawkes; Nic Fishman; Mel Andrews; Zachary C. Lipton (NeurIPS, 2024)

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. - Jake Fawkes; Robert Hu; Robin J. Evans 002; Dino Sejdinovic (Trans. Mach. Learn. Res., 2024)

The Hardness of Validating Observational Studies with Experimental Data. - Jake Fawkes; Michael O'Riordan; Athanasios Vlontzos; Oriol Corcoll; Ciarán Mark Gilligan-Lee (AISTATS, 2025)

Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition. - Jake Fawkes; Lucile Ter-Minassian; Desi R. Ivanova; Uri Shalit; Christopher C. Holmes (AISTATS, 2025)