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CPDAG Recovery on Synthetic Linear SEMs p=10000, d=5, n=p

CPDAG Recovery on Synthetic Linear SEMs p=10000, d=5, n=p

63.3F1 ScoreSCOPE

Updated 6mo ago

Evaluation results

Method	Links
SCOPE2026.05		63.3	1.322
FROSTY2026.05		2.3	85.968

State-of-the-art paper

SCOPE

Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale

Dataset

Linear SEMs

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Related benchmarks

Causal Structure Learning on Theoretical Analysis Transcription Factor (TF) to target path discovery validation (one-step) on TCGA breast cancer mRNA expression dataset GRCh38 reference genome (Additional random partitions of features)Transcription Factor Path Discovery Validation on TCGA Breast Cancer mRNA Expression CPDAG Recovery on High-dimensional SEMs (Uniform Noise, d=5, p=10^4)CPDAG Recovery on High-dimensional synthetic SEMs (Normal noise, d=5)

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