971 citations · 3k across the 74 of their papers we have counts for
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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…
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation
Diego Martinez-Taboada, Dino Sejdinovic
The counterfactual distribution models the effect of the treatment in the untreated group. While most of the work focuses on the expected values of the treatment effect, one may be…
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings
Diego Martinez-Taboada, Dino Sejdinovic
The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. I…
Explaining Preferences with Shapley Values
Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas +1
While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propos…
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Veit D. Wild, Robert Hu, Dino Sejdinovic
We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). G…
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…