{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01206541,0.001146095,0.001116102,0.001128196,0.001229816,0.001771488,0.002551501,0.001643815,0.003901225],"category_scores_gemma":[0.08929535,0.0008429495,0.001125966,0.0009393123,0.002652089,0.00432667,0.004643942,0.003543573,0.001072889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009346214,"about_ca_system_score_gemma":0.002203992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001978038,"about_ca_topic_score_gemma":0.003709027,"domain_scores_codex":[0.9945192,0.002687706,0.0002551119,0.001139549,0.001082471,0.0003160322],"domain_scores_gemma":[0.9597604,0.0222594,0.002003851,0.0130807,0.002345804,0.0005498705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001144745,0.0004788128,0.04581451,0.0008974914,0.0004951141,0.001034998,0.001771723,0.2989873,0.02278551,0.1510336,0.01530448,0.4602518],"study_design_scores_gemma":[0.00008720563,0.0002407083,0.003677948,0.0001795239,0.0001063075,0.0004539002,0.0002396716,0.7974815,0.02170931,0.1671109,0.008645816,0.0000670556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0785961,0.0007189632,0.9123636,0.001291826,0.0001878401,0.000241463,0.0002930615,0.003169671,0.003137418],"genre_scores_gemma":[0.8013454,0.0002927098,0.1937001,0.001104554,0.0001401782,0.0002965545,0.0007547113,0.0005805856,0.001785257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01206541,"threshold_uncertainty_score":0.06380874,"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."}}