no code implementations • 17 Jul 2023 • Jake Fawkes, Robin J. Evans
In this paper we provide a theoretical analysis of counterfactual invariance.
1 code implementation • 26 Jun 2023 • Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz, Robin J. Evans, Chris Holmes
We introduce Path-Wise Shapley effects (PWSHAP), a framework for assessing the targeted effect of a binary (e. g.~treatment) variable from a complex outcome model.
1 code implementation • 9 Dec 2022 • Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic
These improved estimators are inspired by doubly robust estimators of the causal mean, using a similar form within the kernel space.
1 code implementation • 25 Nov 2021 • Robert Hu, Dino Sejdinovic, Robin J. Evans
Causal inference grows increasingly complex as the number of confounders increases.
no code implementations • 7 Aug 2015 • Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans, Peter Bühlmann
We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs).
no code implementations • 9 Jan 2015 • Robin J. Evans
Bayesian network models with latent variables are widely used in statistics and machine learning.
no code implementations • 26 Sep 2013 • Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins
To make modeling and inference with nested Markov models practical, it is necessary to limit the number of parameters in the model, while still correctly capturing the constraints in the marginal of a DAG model.