Logical and algorithmic properties of independence and their application to Bayesian networks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Toward Causal Representation Learning (2021, neutral): “[75], [238] implied by causal structure.”
Towards Causal Representation Learning (2021, background): “While causal structure discovery is hard if we have only two observables [187], the case of more observables is surprisingly easier, the reason being that in that case, there are nontrivial conditional independence properties [238, 51, 74] implied by causal structure.”
Causality for Machine Learning (2019, background): “Surprisingly, this is not the case: the problem in a certain sense becomes easier, and the reason for this is that in that case, there are nontrivial conditional independence properties (Spohn, 1978; Dawid, 1979; Geiger and Pearl, 1990) implied by causal structure.”