68 citations · 287 across the 26 of their papers we have counts for
30 papers
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…
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz +2
Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide…
Combining experimental and observational data through a power likelihood
Xi Lin, Jens Magelund Tarp, Robin J. Evans
Randomized controlled trials are the gold standard for causal inference and play a pivotal role in modern evidence-based medicine. However, the sample sizes they use are often too…
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…
Latent-free equivalent mDAGs
Robin J. Evans
We show that the marginal model for a discrete directed acyclic graph (DAG) with hidden variables is distributionally equivalent to another fully observable DAG model if and only i…
Towards standard imsets for maximal ancestral graphs
Zhongyi Hu, Robin Evans
The imsets of Studený (2005) are an algebraic method for representing conditional independence models. They have many attractive properties when applied to such models, and they ar…