{"id":"W2024255505","doi":"10.6000/1929-6029.2014.03.02.13","title":"Adjusting Complex Heterogeneity in Treatment Assignment in Observational Studies","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pfizer","keywords":"Propensity score matching; Observational study; Estimator; Statistics; Cluster analysis; Weighting; Inverse probability weighting; Regression; Econometrics; Partial least squares regression; Inverse probability; Regression analysis; Mathematics; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1106764,0.0008074151,0.001957651,0.001777562,0.00123751,0.002512443,0.00287701,0.002223079,0.002849713],"category_scores_gemma":[0.2671361,0.0005663551,0.003728951,0.003896388,0.002240835,0.002069861,0.002202588,0.002153332,0.0003412701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785775,"about_ca_system_score_gemma":0.00294871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005129075,"about_ca_topic_score_gemma":0.003698535,"domain_scores_codex":[0.9019982,0.08303126,0.003743375,0.006287108,0.003933434,0.001006718],"domain_scores_gemma":[0.6825175,0.2686641,0.02405006,0.02087189,0.003265794,0.0006305654],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001431039,0.0004878885,0.1554492,0.001985762,0.006298916,0.001567233,0.002225975,0.332089,0.002275357,0.2393075,0.0060784,0.2508038],"study_design_scores_gemma":[0.001292715,0.0008758025,0.03590969,0.0005050878,0.002302443,0.0006866016,0.0005234845,0.5193001,0.002289517,0.4275734,0.008479255,0.0002618929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07731473,0.001929564,0.9159342,0.001605615,0.0002080651,0.001054306,0.0005708875,0.0003142696,0.001068499],"genre_scores_gemma":[0.7966609,0.0009358511,0.1983539,0.0007733536,0.0001935613,0.00117531,0.0007230762,0.00007986195,0.001104224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8893237,"threshold_uncertainty_score":0.5853195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7635707781039781,"score_gpt":0.6555082445573583,"score_spread":0.1080625335466198,"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."}}