210 citations · 653 across the 17 of their papers we have counts for
4 papers · 1 filter
Integrating overlapping datasets using bivariate causal discovery
Anish Dhir, Ciarán M. Lee
Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can…
Compositional resource theories of coherence
John H. Selby, Ciarán M. Lee
Quantum coherence is one of the most important resources in quantum information. Indeed, preventing the loss of coherence is one of the most important technical challenges obstruct…
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
Yura Perov, Logan Graham, Kostis Gourgoulias +4
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programmin…
Counterfactual diagnosis
Jonathan G. Richens, Ciaran M. Lee, Saurabh Johri
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases \e…