{
 "generated": "2026-09-25",
 "packages": {
  "Andrey": "0.1.0",
  "causal-learn": "0.1.4.8",
  "gCastle": "1.0.4",
  "lingam": "1.12.2"
 },
 "environments": {
  "Andrey": "Python 3.14, NumPy 2.4.6",
  "causal-learn, gCastle, lingam": "Python 3.13, NumPy 2.5.2"
 },
 "hardware": "16 cores of a shared AMD EPYC 9555 machine; fits ran in parallel, one per core; one BLAS thread per fit",
 "timing": "seconds per fit after one warm-up fit on ASIA for imports and first-call costs: median of 20 fits on Sachs, 3 on ASIA and ALARM, 1 on HEPAR2 and ANDES; each fit's process stopped after 1,800 s of wall time per timed fit plus 300 s for imports and the warm-up fit, 2,100 s on HEPAR2 and ANDES",
 "settings": {
  "PC, FCI": "Fisher-z, alpha 0.05",
  "GES": "Andrey lambda_value 1.0 (causal-learn 0.5 on its scale), max parents d/2; gCastle BIC with no penalty setting",
  "BOSS, GRaSP": "Andrey lambda_value 1.0 (causal-learn 0.5), search seed 0; GRaSP depth 3",
  "DirectLiNGAM": "pairwise likelihood",
  "ICA-LiNGAM": "FastICA, seed 0, 1,000 iterations",
  "scoring": "PC, GES, BOSS, GRaSP against the truth's CPDAG; FCI by endpoint SHD against its PAG; LiNGAM methods against the DAG",
  "bic": "total BIC under Andrey's lambda_value 1.0 (equal to causal-learn's 0.5); lower is better"
 },
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