{"id":"W2284187917","doi":"10.1186/s12874-016-0119-1","title":"Head to head comparison of the propensity score and the high-dimensional propensity score matching methods","year":2016,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; University of British Columbia; Centre Hospitalier de l’Université de Montréal; McGill University; McMaster University; McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Propensity score matching; Confounding; Medicine; Matching (statistics); Cohort; Context (archaeology); Confidence interval; Cohort study; Internal medicine; Statistics; Mathematics; Pathology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07210784,0.0002612158,0.001316057,0.0001689845,0.0004119362,0.00002376516,0.001213113,0.0003558075,0.0002388617],"category_scores_gemma":[0.2223634,0.0001042613,0.0001333911,0.0005264482,0.003892544,0.0001063557,0.002790788,0.001411028,0.00001401488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001254385,"about_ca_system_score_gemma":0.0008731306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009765957,"about_ca_topic_score_gemma":0.002765061,"domain_scores_codex":[0.9540808,0.04094823,0.001005403,0.0007242552,0.002323078,0.0009182821],"domain_scores_gemma":[0.8843879,0.1128105,0.0002875128,0.001223296,0.0008272503,0.0004636348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003391191,0.0003531001,0.02721664,0.0006163572,0.0001090654,0.0000237552,0.001674384,0.000005585768,0.07140673,0.6145967,0.003108137,0.2774983],"study_design_scores_gemma":[0.001434701,0.0004179114,0.02795094,0.001144169,0.00003213374,0.00008739744,0.0001255658,0.0001812741,0.06975344,0.8983155,0.0003528824,0.0002040573],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.503865,0.00009819942,0.4880672,0.006489794,0.0001154507,0.001199442,0.000004123753,0.00005546447,0.0001052615],"genre_scores_gemma":[0.3814385,0.00002940672,0.6176556,0.0003466539,0.00009682463,0.000141915,6.471278e-7,0.00002902173,0.0002614591],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2837188,"threshold_uncertainty_score":0.9988183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8646910788517377,"score_gpt":0.6556494814906167,"score_spread":0.2090415973611209,"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."}}