This paper introduces a new graphical framework for understanding stable blankets in causal models that include hidden variables and cycles. The research extends existing methods by using acyclic directed mixed graphs (ADMGs) and directed graphs (DGs) to characterize Markov blankets and intervention-stable predictor sets. The findings provide graphical conditions for when a response variable is conditionally independent of intervention variables given a specific predictor set, even in complex causal structures. AI
IMPACT Extends graphical interpretations of stabilized regression, potentially improving causal discovery in complex systems.
RANK_REASON This is a research paper published on arXiv detailing new theoretical advancements in causal inference.
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