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New research explores stable blankets in causal models with hidden variables and cycles

By PulseAugur Editorial · [2 sources] ·

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.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores stable blankets in causal models with hidden variables and cycles

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Hanqing Xiang ·

    Stable Blanket with Hidden Variables and Cycles

    arXiv:2605.01856v1 Announce Type: new Abstract: Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different environments. Existing graphical characterizations of the stable blanket are mainl…

  2. arXiv stat.ML TIER_1 English(EN) · Hanqing Xiang ·

    Stable Blanket with Hidden Variables and Cycles

    Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different environments. Existing graphical characterizations of the stable blanket are mainly developed for structural causal models (SCMs) …

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