Local Characterizations of Causal Bayesian Networks

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External Validity: From Do-Calculus to Transportability Across Populations (2014, background): “An example of the former is contrived in Bareinboim and Pearl (2012), where an alternative, necessary and suﬃcient condition is established in both graphical and algorithmic form.”
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions (2019, unknown): “To the best of our knowledge, this type of constraints appeared ﬁrst at the very deﬁnition of causal Bayesian networks (CBNs) in [1] and then were leveraged to design e ﬃ cient experiments to learn the causal graph in [11].”
On Pearl’s Hierarchy and the Foundations of Causal Inference (2022, background): “28 The assumptions that the causal diagram encodes about the SCM impose con straints not only over the ℒ 1 -distribution P but also over all the interventional ( ℒ 2 ) distributions as encapsulated in the following definition [Bareinboim et al. 2012].”
A General Algorithm for Deciding Transportability of Experimental Results (2013, background): “8 These invariance assumptions are analogous to the missing-arrows in the causal graphs [25] which allow one to identify causal-effects from observational data.”