4 citations · 5 across the 13 of their papers we have counts for
13 papers
Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors
Jake Fawkes, Liam Hodgson, Jason Hartford
Predicting the effect of an unseen gene knockout perturbation on transcriptomic gene expression remains a highly challenging problem for virtual cell models. Recent progress has be…
Towards Autonomous Mechanistic Reasoning in Virtual Cells
Yunhui Jang, Lu Zhu, Jake Fawkes +3
Large language models (LLMs) have recently gained significant attention as a promising approach to accelerate scientific discovery. However, their application in open-ended scienti…
-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
Jake Fawkes, Jason Hartford
In GFlowNets and variational inference, it has been shown that the mean square error between target and model log probabilities is an effective, low variance, surrogate loss for tr…
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…
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
Jake Fawkes, Nic Fishman, Mel Andrews +1
Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair". Real-world data, however, are typica…
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…
The Role of Learning Algorithms in Collective Action
Omri Ben-Dov, Jake Fawkes, Samira Samadi +1
Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on as…
Results on Counterfactual Invariance
Jake Fawkes, Robin J. Evans
In this paper we provide a theoretical analysis of counterfactual invariance. We present a variety of existing definitions, study how they relate to each other and what their graph…
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…