{"id":"W4414476142","doi":"10.1002/cjs.70014","title":"Rank‐based estimation of propensity score weights via subclassification","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Weighting; Estimator; Robustness (evolution); Consistency (knowledge bases); Propensity score matching; Estimation; A-weighting","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01362364,0.0007611433,0.001528825,0.002725382,0.000582302,0.001881182,0.001814101,0.0009422528,0.005664195],"category_scores_gemma":[0.05619499,0.0004159166,0.0009587466,0.002114434,0.001517857,0.002656655,0.002353037,0.002470406,0.0009411747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089461,"about_ca_system_score_gemma":0.001628812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002937376,"about_ca_topic_score_gemma":0.002097151,"domain_scores_codex":[0.9944443,0.003436625,0.0002644713,0.0007924646,0.0008804026,0.0001816619],"domain_scores_gemma":[0.9617124,0.02365022,0.004035185,0.007018573,0.002940779,0.00064283],"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":[0.0002375621,0.0002127581,0.01707994,0.0003503378,0.0002317173,0.0001323991,0.0004474944,0.1637401,0.002066528,0.4401173,0.006510325,0.3688736],"study_design_scores_gemma":[0.00003514257,0.00005905333,0.002152759,0.0001162803,0.00004526875,0.00008294313,0.00006491099,0.7032825,0.001364541,0.2870098,0.005758207,0.00002859476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01354604,0.0002179309,0.9844244,0.0001616159,0.0000242565,0.00008163763,0.0001203741,0.0001608863,0.001262854],"genre_scores_gemma":[0.4771237,0.0007444127,0.5141134,0.0003339201,0.0002142758,0.0006137345,0.001035147,0.0002600121,0.005561499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01362364,"threshold_uncertainty_score":0.07204956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.146098360623777,"score_gpt":0.3524692437893049,"score_spread":0.2063708831655279,"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."}}