Jake Fawkes

18 papers A* 3A 2Journal 12Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
Observationally Informed Adaptive Causal Experimental Design.
CoRR
Erdun Gao, Liang Zhang, Jake Fawkes, Aoqi Zuo, Wenqin Liu, Haoxuan Li, Mingming Gong, Dino Sejdinovic
2025 J jnl
Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation.
CoRR
Erdun Gao, Jake Fawkes, Dino Sejdinovic
2025 A conf
Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition.
AISTATS
Jake Fawkes, Lucile Ter-Minassian, Desi R. Ivanova, Uri Shalit, Christopher C. Holmes
2025 A conf
The Hardness of Validating Observational Studies with Experimental Data.
AISTATS
Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos, Oriol Corcoll, Ciarán Mark Gilligan-Lee
2025 J jnl
The Hardness of Validating Observational Studies with Experimental Data.
CoRR
Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos, Oriol Corcoll, Ciarán Mark Gilligan-Lee
2024 J jnl
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects.
Trans. Mach. Learn. Res.
Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic
2024 J jnl
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition.
CoRR
Jake Fawkes, Lucile Ter-Minassian, Desi R. Ivanova, Uri Shalit, Christopher C. Holmes
2024 A* conf
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning.
NeurIPS
Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton
2024 J jnl
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning.
CoRR
Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton
2024 A* conf
The Role of Learning Algorithms in Collective Action.
ICML
Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal
2024 J jnl
The Role of Learning Algorithms in Collective Action.
CoRR
Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal
2023 J jnl
Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach.
Frontiers Artif. Intell.
Robert Grout, Rishab Gupta, Ruby Bryant, Mawada A. Elmahgoub, Yijie Li, Khushbakht Irfanullah, Rahul F. Patel, Jake Fawkes, Catherine Inness
2023 J jnl
Results on Counterfactual Invariance.
CoRR
Jake Fawkes, Robin J. Evans
2023 A* conf
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge.
ICML
Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
2023 J jnl
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge.
CoRR
Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
2022 J jnl
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap.
CoRR
Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic
2022 — conf
Selection, Ignorability and Challenges With Causal Fairness.
CLeaR
Jake Fawkes, Robin J. Evans, Dino Sejdinovic
2022 J jnl
Selection, Ignorability and Challenges With Causal Fairness.
CoRR
Jake Fawkes, Robin J. Evans, Dino Sejdinovic