A conditional independence-based covariate selection method for causal effects is sound and complete in cyclic models due to the invariance of conditional independence assertions under σ-acyclification.
[bayesian analysis in expert systems]: Comment: Graphical models, causality and intervention
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Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation
A conditional independence-based covariate selection method for causal effects is sound and complete in cyclic models due to the invariance of conditional independence assertions under σ-acyclification.