‪Jake Fawkes‬ - ‪Google Scholar‬

Get my own profile

Cited by

	All	Since 2019
Citations	17	17
h-index	2	2
i10-index	1	1

0

10

5

20212022202320241 2 10 3

Public access

2 articles

0 articles

available

not available

Based on funding mandates

Co-authors

Robin EvansProfessor of Statistical Science, University of OxfordVerified email at stats.ox.ac.uk
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Robert HuAmazonVerified email at mansfield.ox.ac.uk
Shahine BouabidUniversity of OxfordVerified email at stats.ox.ac.uk

Jake Fawkes

Jake Fawkes

University Of Oxford

Verified email at stats.ox.ac.uk

Machine Learning Causal Inference


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Selection, Ignorability and Challenges With Causal Fairness J Fawkes, RJ Evans, D Sejdinovic Conference on Causal Learning and Reasoning 177, 275-289, 2022	12	2022
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects J Fawkes, R Hu, RJ Evans, D Sejdinovic arXiv preprint arXiv:2212.04922, 2022	4	2022
Results on Counterfactual Invariance J Fawkes, RJ Evans ICML 2023 Workshop on Spurious Correlations, Invariance and Stability, 2023	1	2023
Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach R Grout, R Gupta, R Bryant, MA Elmahgoub, Y Li, K Irfanullah, RF Patel, ... Frontiers in Artificial Intelligence 6, 1287541, 2024		2024
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge S Bouabid, J Fawkes, D Sejdinovic International Conference of Machine Learning 202, 2885--2913, 2023		2023

The system can't perform the operation now. Try again later.

Articles 1–5