68 citations · 287 across the 26 of their papers we have counts for
6 papers · 1 filter
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…
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…
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Robert Hu, Dino Sejdinovic, Robin J. Evans
Causal inference grows increasingly complex as the number of confounders increases. Given treatments , confounders and outcomes , we develop a non-parametric method to te…
Parameterizing and Simulating from Causal Models
Robin J. Evans, Vanessa Didelez
Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object…
Nested Markov Properties for Acyclic Directed Mixed Graphs
Thomas S. Richardson, Robin J. Evans, James M. Robins +1
Conditional independence models associated with directed acyclic graphs (DAGs) may be characterized in at least three different ways: via a factorization, the global Markov propert…
Maximum likelihood fitting of acyclic directed mixed graphs to binary data
Robin J. Evans, Thomas S. Richardson
Acyclic directed mixed graphs, also known as semi-Markov models represent the conditional independence structure induced on an observed margin by a DAG model with latent variables.…