A causal discovery protocol using per-edge RESOLVED/IMPOSSIBLE certificates and gated tiers (LSNM, IGCI, Stein, MDL, PEIT) plus meta-hub and node-children oracle queries to achieve a 1+K expert interaction upper bound for any DAG.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
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Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the $1+K$ Lower Bound
A causal discovery protocol using per-edge RESOLVED/IMPOSSIBLE certificates and gated tiers (LSNM, IGCI, Stein, MDL, PEIT) plus meta-hub and node-children oracle queries to achieve a 1+K expert interaction upper bound for any DAG.
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Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.