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StableRCA is a novel root cause analysis framework that identifies intervention targets by estimating local Markov boundaries and detecting conditional distribution shifts, avoiding the need for global causal graph discovery and demonstrating robustness across synthetic and real-world datasets.
This paper evaluates the practical effectiveness of Markov boundaries for tabular prediction, finding that while theoretically optimal, current causal discovery methods fail to consistently improve predictive performance due to computational limitations and mismatched optimization goals.