4 citations · 5 across the 13 of their papers we have counts for
7 papers · 1 filter
Observationally Informed Adaptive Causal Experimental Design
Erdun Gao, Liang Zhang, Jake Fawkes +5
Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data is often available, it is ut…
Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Erdun Gao, Jake Fawkes, Dino Sejdinovic
Estimating the Conditional Average Treatment Effect (CATE) is often constrained by the high cost of obtaining outcome measurements, making active learning essential. However, conve…
The Hardness of Validating Observational Studies with Experimental Data
Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos +2
Observational data is often readily available in large quantities, but can lead to biased causal effect estimates due to the presence of unobserved confounding. Recent works attemp…
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova +2
Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which…
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
A directed acyclic graph (DAG) provides valuable prior knowledge that is often discarded in regression tasks in machine learning. We show that the independences arising from the pr…
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects
Jake Fawkes, Robert Hu, Robin J. Evans +1
With the widespread application of causal inference, it is increasingly important to have tools which can test for the presence of causal effects in a diverse array of circumstance…
Selection, Ignorability and Challenges With Causal Fairness
Jake Fawkes, Robin Evans, Dino Sejdinovic
In this paper we look at popular fairness methods that use causal counterfactuals. These methods capture the intuitive notion that a prediction is fair if it coincides with the pre…