{"id":"W2164498941","doi":"10.1002/sim.2580","title":"A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1134,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto","funders":"","keywords":"Propensity score matching; Confounding; Observational study; Outcome (game theory); Statistics; Matching (statistics); Medicine; Average treatment effect; Variables; Econometrics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04845132,0.0008524176,0.001680212,0.001426422,0.0007175,0.001347724,0.001399815,0.001813798,0.001168846],"category_scores_gemma":[0.1220498,0.0007098514,0.002424733,0.001186239,0.001682484,0.001906955,0.001131405,0.001636814,0.0001568893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001986346,"about_ca_system_score_gemma":0.00167404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007454513,"about_ca_topic_score_gemma":0.003993871,"domain_scores_codex":[0.9815192,0.01597861,0.0005199485,0.0008710177,0.0007402697,0.0003708512],"domain_scores_gemma":[0.740361,0.2402902,0.005693611,0.009473369,0.003088922,0.001092955],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006049864,0.001247361,0.04852712,0.0002950386,0.002514031,0.0002295064,0.0006461812,0.8653165,0.001168928,0.03875001,0.001361311,0.03389415],"study_design_scores_gemma":[0.0004035115,0.00111595,0.007384622,0.00008313108,0.0002840767,0.0001026954,0.000104505,0.9747498,0.0009780137,0.01404984,0.0006708329,0.00007308325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8463221,0.001526185,0.147948,0.000655163,0.00006792765,0.0006625701,0.0003037346,0.0001557298,0.002358643],"genre_scores_gemma":[0.9491639,0.0005614044,0.04845314,0.0002611997,0.00003691712,0.0004652184,0.000405913,0.00005329924,0.0005989801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9515487,"threshold_uncertainty_score":0.2562381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2043349314626578,"score_gpt":0.4063998310840797,"score_spread":0.2020648996214219,"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."}}