{
 "release": "0.8.0",
 "generated": "2026-10-22",
 "theme": "nonlinear conditional-independence tests",
 "highlights": [
  "KCI, RCoT, CMIknn, and GPDC inside PC and FCI, against causal-learn, pcalg with RCIT, and tigramite, on nonlinear data: accuracy on 20 datasets per size, speed on 5 in 3 alternating rounds",
  "the four tests query by query: level, power, and time per query against their reference implementations",
  "Andrey's KCI and CMIknn on an NVIDIA GPU against Andrey's CPU fits of the same datasets, with the same graph on every dataset",
  "BOSS and GRaSP with the BDeu score against causal-learn on categorical data, with the categorical BIC beside",
  "PC and FCI with the batched chi-square and G-square tests against the 0.7.0 build",
  "FCI's and GFCI's feasible sizes: coverage under the cap and errors against the empty graph up to 800 variables"
 ],
 "andrey_builds": [
  {
   "build": "pre-release Andrey 0.7.0 build, 18 October 2026",
   "solutions": [
    "andrey070.fci.chisq",
    "andrey070.fci.gsq",
    "andrey070.pc.chisq",
    "andrey070.pc.gsq"
   ]
  },
  {
   "build": "pre-release Andrey 0.8.0 build, 18 October 2026, with the batched categorical tests",
   "solutions": [
    "andrey.fci.chisq",
    "andrey.fci.gsq",
    "andrey.pc.chisq",
    "andrey.pc.gsq"
   ]
  },
  {
   "build": "pre-release Andrey 0.8.0 build, 19 October 2026",
   "solutions": [
    "andrey.boss.bdeu",
    "andrey.grasp.bdeu"
   ]
  },
  {
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "solutions": [
    "andrey.fci.kci.numpy",
    "andrey.pc.cmiknn.numpy",
    "andrey.pc.gpdc.numpy",
    "andrey.pc.kci.numpy"
   ]
  },
  {
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "solutions": [
    "andrey.fci.rcot.numpy",
    "andrey.pc.rcot.numpy"
   ]
  },
  {
   "build": "pre-release Andrey 0.8.0 build, 22 October 2026",
   "solutions": [
    "andrey.boss.bdeu",
    "andrey.grasp.bdeu"
   ]
  }
 ],
 "regime": {
  "anm_er": {
   "name": "anm_er",
   "description": "Erdos-Renyi DAG with mean degree 2, additive noise x = sum w (p + sin p) + e with Gaussian noise, standardized: near-monotone effects, where Fisher-Z stays competitive"
  },
  "nonmonotone_er": {
   "name": "nonmonotone_er",
   "description": "the same graphs with x = sum w cos(p) + e: even effects that partial correlation does not see"
  },
  "discrete_er": {
   "name": "discrete_er",
   "description": "Erdos-Renyi DAG with mean degree 2, categorical variables with 2 or 3 categories and Dirichlet(1) conditional tables, n = 2,000 rows"
  },
  "diabetes": {
   "name": "diabetes",
   "description": "the diabetes network from the bnlearn repository (413 variables), 5,000 rows sampled from its own tables"
  },
  "bnlearn_networks": {
   "name": "bnlearn_networks",
   "description": "the alarm (37 variables), hepar2 (70), and andes (223) networks from the bnlearn repository, 1,000 and 5,000 rows sampled from their own tables with seed 0, in the network's column order"
  },
  "latent_gauss_er": {
   "name": "latent_gauss_er",
   "description": "Erdos-Renyi DAG, linear-Gaussian, standardized, with 10% or 20% of the nodes hidden, mean degree 2 or 4, and n = 10 d or 50 d rows"
  }
 },
 "column_order": "every dataset lists its variables in an order drawn from its seed, with the true graph permuted the same way; the bnlearn network timing rows keep the network's column order",
 "seeds_per_setting": 20,
 "timed_seeds_per_setting": 5,
 "speed_floor_s": 0.1,
 "error_metric": "each entry's own, in its error_metric",
 "hardware": {
  "nonlinear": {
   "cpu": [
    "AMD EPYC 7713, 4 cores",
    "AMD EPYC 9534, 4 cores",
    "AMD EPYC 9555, 4 cores",
    "Intel Xeon Gold 5418Y, 4 cores",
    "Intel Xeon Gold 6542Y, 4 cores"
   ],
   "accuracy_cpu": [
    "AMD EPYC 7713 and AMD EPYC 9534, 4 cores",
    "AMD EPYC 7713, 4 cores"
   ],
   "cores_per_fit": 4,
   "fits": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps"
  },
  "nonlinear-backup": {
   "cpu": "Intel Core i7-7820X, 2 cores (4 threads)",
   "cores_per_fit": 4,
   "fits": "3 cells at once, each on its own 2 physical cores and their hyperthread siblings"
  },
  "nonlinear-gpu": {
   "gpu": "NVIDIA A100-SXM4-80GB",
   "host_cpu": "AMD EPYC 7413",
   "cores_per_fit": 4
  },
  "boss-grasp": {
   "random_graphs": {
    "cpu": "AMD EPYC 7713",
    "cores_per_fit": 4,
    "fits": "one at a time per job on 4 pinned cores of a node shared with other jobs",
    "python": "3.13.11",
    "packages": {
     "causal-learn": "0.1.4.8",
     "numpy": "2.5.4",
     "scipy": "1.18.1"
    }
   },
   "diabetes": {
    "cpu": "Intel Core i7-7820X",
    "cores_per_fit": 4,
    "fits": "one at a time on 4 pinned cores",
    "python": "3.13.16",
    "packages": {
     "causal-learn": "0.1.4.8",
     "numpy": "2.5.4",
     "scipy": "1.18.1"
    }
   }
  },
  "categorical": {
   "cpu": "AMD EPYC 9534 64-Core Processor",
   "cores_per_fit": 128,
   "fits": "one at a time on an exclusive node, no core budget"
  },
  "boundary": {
   "cpu": "AMD EPYC 7713 64-Core Processor",
   "cores_per_fit": 4,
   "fits": "one at a time on an exclusive node"
  }
 },
 "threads": "a 4-core fit runs with 4 BLAS and OpenMP threads; inside PC's skeleton Andrey pins RCoT, CMIknn, and GPDC to one thread and KCI below 2,000 rows; pcalg runs in one R session; the categorical-test rows run with every core of the node visible",
 "packages": {
  "causal-learn": "0.1.4.8",
  "tigramite": "5.2.10.1",
  "pcalg": "2.7.12",
  "RCIT": "0.1.0 (ericstrobl/RCIT, May 2019)",
  "R": "4.6.1",
  "rpy2": "3.6.9",
  "tetrad": "causal-cmd 1.12.0 (tetrad-lib 7.6.3)"
 },
 "parity_rule": "paired bootstrap",
 "promotion": {
  "rule": "a test is promoted to supported when, against each of its references, (1) the mean over every paired dataset of D = SHD(Andrey) - SHD(reference) is at most 0 and (2) no cell (search, data, d, n) has its percentile bootstrap 95% interval of the mean D entirely above 0 (10,000 resamples); pairs are datasets both finished, a timeout is coverage; the paired t interval is reported beside. KCI, CMIknn, and GPDC are judged on one fit per dataset (rule: single draw); RCoT by the multi-draw rule",
  "multi_draw_rule": "for RCoT, whose packages draw random features: every dataset of every RCoT cell of the accuracy runs (PC and FCI, both data, every d and n; none dropped or chosen) is fit 4 times per package with draw seed 1000 x dataset seed + k (k = 0 to 3); D per dataset is the mean over the 4 draws of SHD(Andrey) minus the mean over the 4 draws of SHD(reference), over the datasets where all 8 fits finished, and conditions (1) and (2) apply to these D unchanged. The rule was registered on 21 October 2026, before any multi-draw result; single_draw keeps the one-draw result beside it",
  "tests": {
   "KCI": {
    "rule": "single draw",
    "references": {
     "causal-learn": {
      "cells": 28,
      "partial_cells": [],
      "pairs": 521,
      "pooled_mean_D": -0.22072936660268713,
      "pooled_bootstrap_95": [
       -0.34745681381957766,
       -0.09788867562380038
      ],
      "pooled_t_95": [
       -0.349478535420637,
       -0.09198019778473729
      ],
      "cells_above_0_bootstrap": [],
      "cells_above_0_t": [],
      "regimes_pooled_cells_above_0": 0,
      "regimes_pooled_cells": 14,
      "andrey_finished": 556,
      "andrey_timeouts": 4,
      "reference_finished": 522,
      "reference_timeouts": 38,
      "reference_not_yet_run": 0,
      "started": 560,
      "pass": true,
      "pass_t": true
     }
    },
    "promote": true,
    "promote_t": true,
    "provisional": false
   },
   "RCoT": {
    "rule": "multi-draw",
    "references": {
     "causal-learn": {
      "draws": 4,
      "cells": 40,
      "pairs": 800,
      "pooled_mean_D": -1.5478125,
      "pooled_bootstrap_95": [
       -1.7393828125,
       -1.3631171875
      ],
      "pooled_t_95": [
       -1.7376665745852404,
       -1.3579584254147596
      ],
      "cells_above_0_bootstrap": [],
      "cells_above_0_t": [],
      "andrey_fits": 3200,
      "andrey_finished": 3200,
      "andrey_timeouts": 0,
      "reference_fits": 3200,
      "reference_finished": 3200,
      "reference_timeouts": 0,
      "pass": true,
      "pass_t": true
     },
     "pcalg": {
      "draws": 4,
      "cells": 32,
      "pairs": 640,
      "pooled_mean_D": -5.029296875,
      "pooled_bootstrap_95": [
       -5.62734375,
       -4.45390625
      ],
      "pooled_t_95": [
       -5.610585266303236,
       -4.448008483696764
      ],
      "cells_above_0_bootstrap": [],
      "cells_above_0_t": [],
      "andrey_fits": 2560,
      "andrey_finished": 2560,
      "andrey_timeouts": 0,
      "reference_fits": 2560,
      "reference_finished": 2560,
      "reference_timeouts": 0,
      "pass": true,
      "pass_t": true
     }
    },
    "promote": true,
    "promote_t": true,
    "provisional": false,
    "single_draw": {
     "causal-learn": {
      "cells": 40,
      "partial_cells": [],
      "pairs": 800,
      "pooled_mean_D": -1.44125,
      "pooled_bootstrap_95": [
       -1.7125,
       -1.17375
      ],
      "pooled_t_95": [
       -1.709342830683216,
       -1.173157169316784
      ],
      "cells_above_0_bootstrap": [
       {
        "search": "pc",
        "regime": "anm_er",
        "d": 10,
        "n": 5000,
        "pairs": 20,
        "mean_D": 0.3,
        "bootstrap_95": [
         0.05,
         0.65
        ],
        "t_95": [
         -0.042911860939675694,
         0.6429118609396757
        ],
        "andrey_worse": 4,
        "reference_worse": 0,
        "partial": false
       },
       {
        "search": "fci",
        "regime": "anm_er",
        "d": 5,
        "n": 500,
        "pairs": 20,
        "mean_D": 0.55,
        "bootstrap_95": [
         0.05,
         1.2
        ],
        "t_95": [
         -0.06632549150575151,
         1.1663254915057517
        ],
        "andrey_worse": 4,
        "reference_worse": 0,
        "partial": false
       }
      ],
      "cells_above_0_t": [],
      "regimes_pooled_cells_above_0": 0,
      "regimes_pooled_cells": 20,
      "andrey_finished": 800,
      "andrey_timeouts": 0,
      "reference_finished": 800,
      "reference_timeouts": 0,
      "reference_not_yet_run": 0,
      "started": 800
     },
     "pcalg": {
      "cells": 32,
      "partial_cells": [],
      "pairs": 640,
      "pooled_mean_D": -4.6421875,
      "pooled_bootstrap_95": [
       -5.253125,
       -4.06875
      ],
      "pooled_t_95": [
       -5.224860488373022,
       -4.059514511626979
      ],
      "cells_above_0_bootstrap": [],
      "cells_above_0_t": [],
      "regimes_pooled_cells_above_0": 0,
      "regimes_pooled_cells": 16,
      "andrey_finished": 640,
      "andrey_timeouts": 0,
      "reference_finished": 640,
      "reference_timeouts": 0,
      "reference_not_yet_run": 0,
      "started": 640
     }
    }
   },
   "CMIknn": {
    "rule": "single draw",
    "references": {
     "tigramite": {
      "cells": 12,
      "partial_cells": [],
      "pairs": 238,
      "pooled_mean_D": -0.7016806722689075,
      "pooled_bootstrap_95": [
       -0.9285714285714286,
       -0.48739495798319327
      ],
      "pooled_t_95": [
       -0.9217398380388937,
       -0.48162150649892127
      ],
      "cells_above_0_bootstrap": [],
      "cells_above_0_t": [],
      "regimes_pooled_cells_above_0": 0,
      "regimes_pooled_cells": 6,
      "andrey_finished": 240,
      "andrey_timeouts": 0,
      "reference_finished": 238,
      "reference_timeouts": 2,
      "reference_not_yet_run": 0,
      "started": 240,
      "pass": true,
      "pass_t": true
     }
    },
    "promote": true,
    "promote_t": true,
    "provisional": false
   },
   "GPDC": {
    "rule": "single draw",
    "references": {
     "tigramite": {
      "cells": 14,
      "partial_cells": [],
      "pairs": 268,
      "pooled_mean_D": -1.2313432835820894,
      "pooled_bootstrap_95": [
       -1.4962686567164178,
       -0.9775186567164194
      ],
      "pooled_t_95": [
       -1.4929884262462085,
       -0.9696981409179704
      ],
      "cells_above_0_bootstrap": [],
      "cells_above_0_t": [],
      "regimes_pooled_cells_above_0": 0,
      "regimes_pooled_cells": 7,
      "andrey_finished": 280,
      "andrey_timeouts": 0,
      "reference_finished": 268,
      "reference_timeouts": 12,
      "reference_not_yet_run": 0,
      "started": 280,
      "pass": true,
      "pass_t": true
     }
    },
    "promote": true,
    "promote_t": true,
    "provisional": false
   }
  }
 },
 "complete": false,
 "pending_fits": 180,
 "incomplete": [
  {"lane":"nonlinear-backup","method":"PC with the KCI test","comparator":"causal-learn","regime":"anm_er","d":20,"n":1000,"missing":"timing running or queued on the backup workstation"},
  {"lane":"nonlinear-backup","method":"PC with the KCI test","comparator":"causal-learn","regime":"nonmonotone_er","d":20,"n":1000,"missing":"timing running or queued on the backup workstation"},
  {"lane":"nonlinear-backup","method":"PC with the KCI test","comparator":"causal-learn","regime":"anm_er","d":10,"n":1000,"missing":"timing running or queued on the backup workstation"},
  {"lane":"nonlinear A100","method":"PC with the KCI test","regime":"anm_er","d":20,"n":1000,"missing":"no CPU timing of this size yet"},
  {"lane":"nonlinear A100","method":"PC with the KCI test","regime":"nonmonotone_er","d":20,"n":1000,"missing":"no CPU timing of this size yet"},
  {"lane":"nonlinear A100","method":"PC with the KCI test","regime":"anm_er","d":20,"n":2000,"missing":"the CPU fit passed its cap on a timed dataset, so the GPU graph could not be checked on it"}
 ],
 "methods": [
  {
   "method": "PC with the KCI test",
   "setting": "alpha=0.05, indep_test='kci'",
   "solution": "andrey.pc.kci.numpy",
   "comparator": "causal-learn",
   "comparator_solution": "causal-learn.pc.kci",
   "comparator_setting": "alpha=0.05, indep_test='kci'",
   "regime": "anm_er",
   "lane": "nonlinear",
   "reference_order": "causal-learn's pc() orients the colliders in column order and keeps the first of two that conflict (uc_rule=0, uc_priority=2)",
   "column_solution": "andrey.pc.kci.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "causal-learn's PC with CIT(data, 'kci'): KCI_CInd with its gamma approximation; it drops null eigenvalues below 1e-5, which Andrey keeps. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's pc() orients the colliders in column order and keeps the first of two that conflict (uc_rule=0, uc_priority=2)), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores; AMD EPYC 9534, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": false,
   "rows": [
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   ]
  },
  {
   "method": "PC with the KCI test",
   "setting": "alpha=0.05, indep_test='kci'",
   "solution": "andrey.pc.kci.numpy",
   "comparator": "causal-learn",
   "comparator_solution": "causal-learn.pc.kci",
   "comparator_setting": "alpha=0.05, indep_test='kci'",
   "regime": "nonmonotone_er",
   "lane": "nonlinear",
   "reference_order": "causal-learn's pc() orients the colliders in column order and keeps the first of two that conflict (uc_rule=0, uc_priority=2)",
   "column_solution": "andrey.pc.kci.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "causal-learn's PC with CIT(data, 'kci'): KCI_CInd with its gamma approximation; it drops null eigenvalues below 1e-5, which Andrey keeps. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's pc() orients the colliders in column order and keeps the first of two that conflict (uc_rule=0, uc_priority=2)), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores; AMD EPYC 9534, 4 cores; AMD EPYC 9555, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": false,
   "rows": [
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  {
   "method": "FCI with the KCI test",
   "setting": "alpha=0.05, collider_rule='sepsets', indep_test='kci'",
   "solution": "andrey.fci.kci.numpy",
   "comparator": "causal-learn",
   "comparator_solution": "causal-learn.fci.kci",
   "comparator_setting": "alpha=0.05, indep_test='kci'",
   "regime": "anm_er",
   "lane": "nonlinear",
   "reference_order": "causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order",
   "column_solution": "andrey.fci.kci.column.numpy",
   "column_setting": "possible_dsep='fast', with the orientation rules one firing at a time in column order",
   "note": "causal-learn's FCI with CIT(data, 'kci'): KCI_CInd with its gamma approximation; it drops null eigenvalues below 1e-5, which Andrey keeps. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order), so Andrey's FCI also ran in its column-order mode (possible_dsep='fast', with the orientation rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated PAG and the true PAG of the observed variables: one per variable pair whose edge is missing, extra, or marked differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 9534, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
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   ]
  },
  {
   "method": "FCI with the KCI test",
   "setting": "alpha=0.05, collider_rule='sepsets', indep_test='kci'",
   "solution": "andrey.fci.kci.numpy",
   "comparator": "causal-learn",
   "comparator_solution": "causal-learn.fci.kci",
   "comparator_setting": "alpha=0.05, indep_test='kci'",
   "regime": "nonmonotone_er",
   "lane": "nonlinear",
   "reference_order": "causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order",
   "column_solution": "andrey.fci.kci.column.numpy",
   "column_setting": "possible_dsep='fast', with the orientation rules one firing at a time in column order",
   "note": "causal-learn's FCI with CIT(data, 'kci'): KCI_CInd with its gamma approximation; it drops null eigenvalues below 1e-5, which Andrey keeps. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order), so Andrey's FCI also ran in its column-order mode (possible_dsep='fast', with the orientation rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated PAG and the true PAG of the observed variables: one per variable pair whose edge is missing, extra, or marked differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (Intel Xeon Gold 6542Y, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": true,
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   ]
  },
  {
   "method": "PC with the RCoT test",
   "setting": "alpha=0.05, indep_test='rcot'",
   "solution": "andrey.pc.rcot.numpy",
   "comparator": "causal-learn",
   "comparator_solution": "causal-learn.pc.rcot",
   "comparator_setting": "alpha=0.05, indep_test='rcot'",
   "regime": "anm_er",
   "lane": "nonlinear",
   "reference_order": "causal-learn's pc() orients the colliders in column order and keeps the first of two that conflict (uc_rule=0, uc_priority=2)",
   "column_solution": "andrey.pc.rcot.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "causal-learn's PC with CIT(data, 'rcit', rcit=False, num_f=100, num_f2=5, approx='hbe'); it draws the random features from its own generator and drops zero distances from the bandwidth. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd; for PC from the Fisher-Z arm on the 20 October build, whose PC with Fisher-Z returns the same graphs as the RCoT build's on every dataset checked). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's pc() orients the colliders in column order and keeps the first of two that conflict (uc_rule=0, uc_priority=2)), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default. Both draw random features, so each accuracy dataset was also fit 4 times per package, draw seed 1000 x dataset seed + k (multi_draw: D per dataset is the mean over the 4 draws)",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
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   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "causal-learn's PC with CIT(data, 'rcit', rcit=False, num_f=100, num_f2=5, approx='hbe'); it draws the random features from its own generator and drops zero distances from the bandwidth. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd; for PC from the Fisher-Z arm on the 20 October build, whose PC with Fisher-Z returns the same graphs as the RCoT build's on every dataset checked). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's pc() orients the colliders in column order and keeps the first of two that conflict (uc_rule=0, uc_priority=2)), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default. Both draw random features, so each accuracy dataset was also fit 4 times per package, draw seed 1000 x dataset seed + k (multi_draw: D per dataset is the mean over the 4 draws)",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": true,
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  },
  {
   "method": "FCI with the RCoT test",
   "setting": "alpha=0.05, collider_rule='sepsets', indep_test='rcot'",
   "solution": "andrey.fci.rcot.numpy",
   "comparator": "causal-learn",
   "comparator_solution": "causal-learn.fci.rcot",
   "comparator_setting": "alpha=0.05, indep_test='rcot'",
   "regime": "anm_er",
   "lane": "nonlinear",
   "reference_order": "causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order",
   "column_solution": "andrey.fci.rcot.column.numpy",
   "column_setting": "possible_dsep='fast', with the orientation rules one firing at a time in column order",
   "note": "causal-learn's FCI with CIT(data, 'rcit', rcit=False, num_f=100, num_f2=5, approx='hbe'); it draws the random features from its own generator and drops zero distances from the bandwidth. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order), so Andrey's FCI also ran in its column-order mode (possible_dsep='fast', with the orientation rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default. Both draw random features, so each accuracy dataset was also fit 4 times per package, draw seed 1000 x dataset seed + k (multi_draw: D per dataset is the mean over the 4 draws)",
   "error_metric": "SHD between the estimated PAG and the true PAG of the observed variables: one per variable pair whose edge is missing, extra, or marked differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 9534, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": true,
   "rows": [
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   ]
  },
  {
   "method": "FCI with the RCoT test",
   "setting": "alpha=0.05, collider_rule='sepsets', indep_test='rcot'",
   "solution": "andrey.fci.rcot.numpy",
   "comparator": "causal-learn",
   "comparator_solution": "causal-learn.fci.rcot",
   "comparator_setting": "alpha=0.05, indep_test='rcot'",
   "regime": "nonmonotone_er",
   "lane": "nonlinear",
   "reference_order": "causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order",
   "column_solution": "andrey.fci.rcot.column.numpy",
   "column_setting": "possible_dsep='fast', with the orientation rules one firing at a time in column order",
   "note": "causal-learn's FCI with CIT(data, 'rcit', rcit=False, num_f=100, num_f2=5, approx='hbe'); it draws the random features from its own generator and drops zero distances from the bandwidth. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (causal-learn's fci() runs its possible-d-separation step and its orientation rules in column order), so Andrey's FCI also ran in its column-order mode (possible_dsep='fast', with the orientation rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default. Both draw random features, so each accuracy dataset was also fit 4 times per package, draw seed 1000 x dataset seed + k (multi_draw: D per dataset is the mean over the 4 draws)",
   "error_metric": "SHD between the estimated PAG and the true PAG of the observed variables: one per variable pair whose edge is missing, extra, or marked differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 9534, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": true,
   "rows": [
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   ]
  },
  {
   "method": "PC with the RCoT test",
   "setting": "alpha=0.05, indep_test='rcot'",
   "solution": "andrey.pc.rcot.numpy",
   "comparator": "pcalg",
   "comparator_solution": "pcalg.pc.rcot",
   "comparator_setting": "alpha=0.05, indep_test='rcot'",
   "regime": "anm_er",
   "lane": "nonlinear",
   "reference_order": "pcalg's pc() runs with solve.confl=FALSE, its default, where a later v-structure overwrites an earlier one in column order",
   "column_solution": "andrey.pc.rcot.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "pcalg's pc() with RCIT's RCoT (approx='hbe', num_f=100, num_f2=5), the implementation RCoT was published with, as its independence test; RIT for an empty conditioning set; RCIT draws the features from its own generator. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd; for PC from the Fisher-Z arm on the 20 October build, whose PC with Fisher-Z returns the same graphs as the RCoT build's on every dataset checked). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (pcalg's pc() runs with solve.confl=FALSE, its default, where a later v-structure overwrites an earlier one in column order), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default. Both draw random features, so each accuracy dataset was also fit 4 times per package, draw seed 1000 x dataset seed + k (multi_draw: D per dataset is the mean over the 4 draws)",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": true,
   "rows": [
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   ]
  },
  {
   "method": "PC with the RCoT test",
   "setting": "alpha=0.05, indep_test='rcot'",
   "solution": "andrey.pc.rcot.numpy",
   "comparator": "pcalg",
   "comparator_solution": "pcalg.pc.rcot",
   "comparator_setting": "alpha=0.05, indep_test='rcot'",
   "regime": "nonmonotone_er",
   "lane": "nonlinear",
   "reference_order": "pcalg's pc() runs with solve.confl=FALSE, its default, where a later v-structure overwrites an earlier one in column order",
   "column_solution": "andrey.pc.rcot.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "pcalg's pc() with RCIT's RCoT (approx='hbe', num_f=100, num_f2=5), the implementation RCoT was published with, as its independence test; RIT for an empty conditioning set; RCIT draws the features from its own generator. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd; for PC from the Fisher-Z arm on the 20 October build, whose PC with Fisher-Z returns the same graphs as the RCoT build's on every dataset checked). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (pcalg's pc() runs with solve.confl=FALSE, its default, where a later v-structure overwrites an earlier one in column order), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default. Both draw random features, so each accuracy dataset was also fit 4 times per package, draw seed 1000 x dataset seed + k (multi_draw: D per dataset is the mean over the 4 draws)",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": true,
   "rows": [
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   ]
  },
  {
   "method": "PC with the CMIknn test",
   "setting": "alpha=0.05, indep_test='cmiknn'",
   "solution": "andrey.pc.cmiknn.numpy",
   "comparator": "tigramite",
   "comparator_solution": "tigramite.pc.cmiknn",
   "comparator_setting": "alpha=0.05, indep_test='cmiknn', k_cmi=0.1, k_perm=5, n_permutations=500",
   "regime": "anm_er",
   "lane": "nonlinear",
   "reference_order": "tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent",
   "column_solution": "andrey.pc.cmiknn.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "tigramite's own PC (run_pcalg_non_timeseries_data, contemp_collider_rule='none') with its CMIknn (knn=0.1, shuffle_neighbors=5, sig_samples=500, shuffle test, rank transform); both p-values are (b + 1) / (B + 1). Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores; Intel Xeon Gold 5418Y, 4 cores; Intel Xeon Gold 6542Y, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": false,
   "rows": [
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   ]
  },
  {
   "method": "PC with the CMIknn test",
   "setting": "alpha=0.05, indep_test='cmiknn'",
   "solution": "andrey.pc.cmiknn.numpy",
   "comparator": "tigramite",
   "comparator_solution": "tigramite.pc.cmiknn",
   "comparator_setting": "alpha=0.05, indep_test='cmiknn', k_cmi=0.1, k_perm=5, n_permutations=500",
   "regime": "nonmonotone_er",
   "lane": "nonlinear",
   "reference_order": "tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent",
   "column_solution": "andrey.pc.cmiknn.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "tigramite's own PC (run_pcalg_non_timeseries_data, contemp_collider_rule='none') with its CMIknn (knn=0.1, shuffle_neighbors=5, sig_samples=500, shuffle test, rank transform); both p-values are (b + 1) / (B + 1). Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores; AMD EPYC 9534, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": false,
   "rows": [
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   ]
  },
  {
   "method": "PC with the GPDC test",
   "setting": "alpha=0.05, indep_test='gpdc'",
   "solution": "andrey.pc.gpdc.numpy",
   "comparator": "tigramite",
   "comparator_solution": "tigramite.pc.gpdc",
   "comparator_setting": "alpha=0.05, indep_test='gpdc'",
   "regime": "anm_er",
   "lane": "nonlinear",
   "reference_order": "tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent",
   "column_solution": "andrey.pc.gpdc.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "tigramite's own PC (run_pcalg_non_timeseries_data, contemp_collider_rule='none') with its GPDC and its analytic null; its own GP kernel and optimizer, and a null that depends only on n, where Andrey draws a rank-permutation null of 1,000 draws inside every timed fit. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": false,
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  },
  {
   "method": "PC with the GPDC test",
   "setting": "alpha=0.05, indep_test='gpdc'",
   "solution": "andrey.pc.gpdc.numpy",
   "comparator": "tigramite",
   "comparator_solution": "tigramite.pc.gpdc",
   "comparator_setting": "alpha=0.05, indep_test='gpdc'",
   "regime": "nonmonotone_er",
   "lane": "nonlinear",
   "reference_order": "tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent",
   "column_solution": "andrey.pc.gpdc.column.numpy",
   "column_setting": "collider_order='column', with Meek's rules one firing at a time in column order",
   "note": "tigramite's own PC (run_pcalg_non_timeseries_data, contemp_collider_rule='none') with its GPDC and its analytic null; its own GP kernel and optimizer, and a null that depends only on n, where Andrey draws a rank-permutation null of 1,000 draws inside every timed fit. Both run the search at alpha 0.05. Accuracy: 20 datasets per size, one fit each; Fisher-Z's errors on the same datasets are beside (fisherz_shd). Speed: 5 of those datasets, 3 rounds, Andrey and the reference alternating per dataset on the same 4 cores. The reference depends on the column order (tigramite's PC runs with contemp_collider_rule='none', which its documentation says is not order-independent), so Andrey's PC also ran in its column-order mode (collider_order='column', with Meek's rules one firing at a time in column order) on the same datasets, one fit each with the same caps, on 4 cores of a shared node (andrey_column_*), as context beside the order-free default",
   "error_metric": "SHD between the estimated CPDAG and the true DAG's CPDAG: one per variable pair whose edge is missing, extra, or oriented differently. The empty graph's SHD is the number of true edges.",
   "hardware": "each row names the CPU its times ran on (AMD EPYC 7713, 4 cores; AMD EPYC 9534, 4 cores)",
   "cores": 4,
   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "fit_protocol": "accuracy: one fit per dataset on 4 pinned cores of a shared node, no warm-up, a 1800 s wall cap (3600 s for CMIknn, KCI at n = 5,000, and GPDC at n = 2,000) and a 30000 MB memory cap per fit; speed: 3 rounds on one 4-core slot, each round fitting Andrey then the reference on every timed dataset, each fit in its own process after 0 or 1 untimed warm-up fit (the row's warmup; 0 where a fit can pass 300 s), the same caps",
   "complete": false,
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   "comparator": "tigramite",
   "comparator_solution": "tigramite.pc.cmiknn",
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   "hardware": "AMD EPYC 7713, 4 cores",
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  {
   "method": "BOSS on discrete data, BDeu score",
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   "hardware": "Intel Core i7-7820X, 4 cores",
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   "fit_protocol": "one fit at a time on an exclusive node, the faster of 2 timed fits per dataset, no warm-up, a 3600 s wall cap per fit",
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   "fit_protocol": "one fit at a time on an exclusive node, the faster of 2 timed fits per dataset, no warm-up, a 3600 s wall cap per fit",
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   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
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  },
  {
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   "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
   "hardware": "each row names its CPU (AMD EPYC 7713, 4 cores; AMD EPYC 9534, 4 cores)"
  },
  {
   "test": "RCoT",
   "lane": "nonlinear queries",
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   "build": "pre-release Andrey 0.8.0 build, 20 October 2026, with quasi-Monte Carlo random Fourier features in RCoT",
   "hardware": "each row names its CPU (AMD EPYC 7713, 4 cores)"
  }
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 "gpu": {
  "device": "NVIDIA A100-SXM4-80GB",
  "host_cpu": "AMD EPYC 7413",
  "build": "pre-release Andrey 0.8.0 build, 20 October 2026",
  "note": "Andrey's PC and FCI with KCI and CMIknn on the GPU (ANDREY_DEVICE='cuda') against Andrey's CPU fits of the same datasets: 5 datasets per size, 3 rounds each. gpu_s and cpu_s are medians over datasets of each dataset's median over rounds; ratio is the median over datasets of CPU time / GPU time, against the CPU fits on the row's cpu (each from that CPU's own timing rounds), with ratio_min and ratio_max over the datasets. A row is published only when every GPU fit finished and returned the CPU build's graph on every dataset (same_graph). A size timed on two CPU models has one row per model; where it was timed twice on one model, the row uses the timing of the size's CPU speed row",
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