{"id":"W2169425545","doi":"10.1002/sim.6276","title":"The use of bootstrapping when using propensity‐score matching without replacement: a simulation study","year":2014,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":278,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Services and Policy Research; University of Toronto; Institute for Work & Health; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Heart and Stroke Foundation of Canada","keywords":"Propensity score matching; Bootstrapping (finance); Statistics; Resampling; Standard error; Matching (statistics); Confidence interval; Sample size determination; Standard deviation; Sampling distribution; Observational study; Econometrics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001554607,0.0001487087,0.000373402,0.00009781491,0.0001142302,0.00002014366,0.0001427852,0.000037873,0.00001455394],"category_scores_gemma":[0.005391769,0.00009968238,0.00001090305,0.0001253245,0.0001730461,0.0001164248,0.0000767536,0.0002275064,3.882496e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007613562,"about_ca_system_score_gemma":0.00002530242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002305582,"about_ca_topic_score_gemma":0.0002849462,"domain_scores_codex":[0.9982763,0.0002454444,0.0006665422,0.0001971884,0.0004172755,0.0001972772],"domain_scores_gemma":[0.995978,0.002935601,0.0004056042,0.0004443667,0.0002007968,0.00003562573],"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":[0.001027624,0.001083299,0.2644673,0.001321241,0.0003142329,0.00006763865,0.05405121,0.05883328,0.01238253,0.5664105,0.001654613,0.03838659],"study_design_scores_gemma":[0.001002178,0.0007180276,0.004290229,0.0008882358,0.0001038814,0.000003660412,0.001268077,0.1875128,0.0001858077,0.8036195,0.0002156431,0.0001919498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3198702,0.000006119692,0.6793033,0.00002631334,0.00004853178,0.0006330695,0.000004784818,0.0000396364,0.00006794722],"genre_scores_gemma":[0.7771273,0.000005420867,0.222706,0.00003370039,0.00004019514,0.00001178463,0.000004000333,0.00002140587,0.00005013562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4572571,"threshold_uncertainty_score":0.645484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3885413863266839,"score_gpt":0.4667305653599856,"score_spread":0.07818917903330169,"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."}}