Introduces separable and essentially separable graphs as a broad class for mixed graphical models, provides multiple characterizations of the graphs and their separation equivalence, and develops an identification algorithm for equivalence classes.
Ancestral Graph
4 Pith papers cite this work, alongside 576 external citations. Polarity classification is still indexing.
4
Pith papers citing it
576
external citations · external index
years
2026 4representative citing papers
Derivation graphs characterize the space of do-calculus equivalent interventional expressions, enable identification with at most four rule applications, and yield multiple valid estimands for improved efficiency.
Causal sufficiency is identified with involutive closure plus Frobenius-copy preservation, but the equivalence is true by definition rather than by derivation.
Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.
citing papers explorer
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Characterizing and Identifying Separable Graphical Models
Introduces separable and essentially separable graphs as a broad class for mixed graphical models, provides multiple characterizations of the graphs and their separation equivalence, and develops an identification algorithm for equivalence classes.
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Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
Derivation graphs characterize the space of do-calculus equivalent interventional expressions, enable identification with at most four rule applications, and yield multiple valid estimands for improved efficiency.
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Infinitesimal Causality
Causal sufficiency is identified with involutive closure plus Frobenius-copy preservation, but the equivalence is true by definition rather than by derivation.
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Towards a holistic understanding of Selection Bias for Causal Effect Identification
Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.