{"id":"W3210561222","doi":"10.1145/3459637.3482380","title":"Pulling Up by the Causal Bootstraps","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research; Vector Institute; Microsoft Research","keywords":"Computer science; Machine learning; Debiasing; Artificial intelligence; Spurious relationship; Causal inference; Bootstrapping (finance); Benchmarking; Causation; Causal model; Data mining; Econometrics; Psychology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000458405,0.0001948119,0.0001825654,0.00004431287,0.000193549,0.001217193,0.001366352,0.0001484704,0.0002795478],"category_scores_gemma":[0.00005518544,0.0001426158,0.0001266924,0.000174505,0.00004244394,0.0001578293,0.001195128,0.0008205118,0.00007438714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003564476,"about_ca_system_score_gemma":0.0002020403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001498166,"about_ca_topic_score_gemma":0.00002517809,"domain_scores_codex":[0.9983571,0.0001562576,0.0002588026,0.0005639723,0.0003912668,0.00027257],"domain_scores_gemma":[0.9987028,0.0001369773,0.0001308516,0.000847882,0.00009249288,0.00008898193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001300597,0.0002216434,0.0006012658,0.0002103499,0.0004827295,0.0001959476,0.02431099,0.03874337,0.003872585,0.3187735,0.1697866,0.442788],"study_design_scores_gemma":[0.0005112884,0.0000377087,0.001011683,0.0001607145,0.0000320637,0.00007519542,0.002425139,0.673615,0.002819088,0.005682667,0.3124827,0.001146841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002472483,0.0006597618,0.9635015,0.00335673,0.001914054,0.0001381868,0.000001679079,0.0002550889,0.0277005],"genre_scores_gemma":[0.9071874,0.0001585798,0.05247504,0.003699543,0.0002415849,0.00004700029,0.00006860537,0.00002994926,0.0360923],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9110265,"threshold_uncertainty_score":0.9998196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616257560238582,"score_gpt":0.2725850856762464,"score_spread":0.2364225100738606,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}