{"id":"W2736383080","doi":"10.1177/0962280217715487","title":"A model averaging approach for estimating propensity scores by optimizing balance","year":2017,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Propensity score matching; Covariate; Estimator; Statistics; Statistic; Nonparametric statistics; Inverse probability weighting; Causal inference; Weighting; Mathematics; Parametric statistics; Econometrics; Parametric model; Computer science; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02397961,0.0002216665,0.0006465929,0.0001378345,0.0007130911,0.0002209044,0.001253887,0.0002613813,0.00009934621],"category_scores_gemma":[0.275305,0.0001848742,0.00004872867,0.0001405178,0.001324869,0.0002388862,0.0008317298,0.001768017,0.0000020042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002022811,"about_ca_system_score_gemma":0.0003330322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007520004,"about_ca_topic_score_gemma":0.000007006952,"domain_scores_codex":[0.9946545,0.00131632,0.0006393786,0.0007134351,0.001592847,0.001083521],"domain_scores_gemma":[0.9793472,0.01878649,0.0001604465,0.0008777438,0.0003743878,0.0004536944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001526417,0.0003198747,0.0002719871,0.0009404214,0.00002103179,0.00005272885,0.0003109914,0.0001864765,0.002425297,0.4955066,0.006750771,0.4930612],"study_design_scores_gemma":[0.0002497905,0.0000496857,0.00001425547,0.0001902208,0.000003672316,0.000003234224,0.00002674605,0.5325156,0.00105236,0.4657582,0.00002300222,0.0001132138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003532746,0.00003361074,0.9924604,0.0004999734,0.00005089419,0.00107789,0.00005241731,0.0001112484,0.005360261],"genre_scores_gemma":[0.01750429,0.00002407518,0.9814073,0.00006557968,0.00008474324,0.0006669,0.00001334929,0.00005256352,0.0001811964],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5323292,"threshold_uncertainty_score":0.8310903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6341758442076512,"score_gpt":0.6615234680562492,"score_spread":0.02734762384859801,"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."}}