Complete Causal Identification from Ancestral Graphs under Selection Bias

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• The paper develops a characterization of conditional independence relations invariant across all causal models represented by a Partial Ancestral Graph (PAG).method 95%
• The developed theory allows for a general measure-theoretic version of Pearl's causal calculus.method 90%
• The paper presents a sound and complete identification algorithm for PAGs under selection bias.method 95%
• The results are applicable to PAGs learned by algorithms integrating observational and experimental data with background knowledge.technical 90%
• Enumerating all graphs in a Markov equivalence class represented by a PAG is infeasible due to exponential growth in the number of graphs.limitation 85%
• The paper introduces a framework for causal Markov Equivalent (MAG) graphs and a broader class termed SOPAGs.method 80%
• The MAG transformation presented in the paper differs slightly from previous work due to the presence of exogenous input nodes.technical 85%

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