| { |
| "id": "ml21-root", |
| "requirements": "A credible experiment comparing causal structure learning methods (PC, GES, NOTEARS-linear, or equivalents) on small synthetic Gaussian linear-SEM DAGs: methods are implemented, runs cover multiple synthetic regimes with multiple seeds, and results address H1/H2/H3 directionally using SHD-centered evidence.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative structure-learning methods or linear-SEM variants that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml21-code", |
| "requirements": "Core causal structure learning conditions and synthetic data generation are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml21-code-methods", |
| "requirements": "The submission implements distinct code paths for multiple structure learning methods \u2014 typically PC, GES, and NOTEARS-linear or equivalents \u2014 rather than reusing one estimator under different names.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml21-code-synth", |
| "requirements": "A reproducible synthetic linear-Gaussian SEM DAG generator is implemented with moderate node counts, including ground-truth adjacency export and sample generation.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml21-code-hparams", |
| "requirements": "At least one tunable hyperparameter is exposed and used for the constraint-based method (e.g., PC's alpha) and the continuous-optimization method (e.g., NOTEARS' sparsity weight), with documented default or grid values.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Hyperparameter Tuning" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml21-exec", |
| "requirements": "The benchmark produces comparable metrics across methods/regimes.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml21-exec-coverage", |
| "requirements": "Execution covers multiple synthetic regimes and reports results for the primary methods at one or more sample sizes.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml21-exec-metrics", |
| "requirements": "A machine-readable results artifact includes numeric SHD for each (method, regime) and at least one additional metric such as edge-F1 or runtime.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml21-exec-seeds", |
| "requirements": "Each reported cell is averaged over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml21-results", |
| "requirements": "Results analysis addresses H1/H2/H3 directionally.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml21-result-h1", |
| "requirements": "The submission compares NOTEARS-linear vs PC at a reasonable sample size and conveys whether NOTEARS achieves meaningfully lower SHD on most regimes \u2014 judge directionally against H1.", |
| "weight": 25.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml21-result-h2h3", |
| "requirements": "The submission conveys qualitative verdicts for H2 and H3 using SHD aggregates, including whether larger sample sizes or denser-graph regimes yield meaningful SHD improvements.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml21-result-writeup", |
| "requirements": "The README or writeup describes setup, reports key SHD/edge-F1/runtime findings, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (synthetic-only scope, small-node regime, hyperparameter sensitivity, finite seeds). No strict word-count requirement.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
|
|
