{"id":"W3210110199","doi":"10.1111/biom.13776","title":"Combining Parametric and Nonparametric Models to Estimate Treatment Effects in Observational Studies","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Observational study; Nonparametric statistics; Econometrics; Parametric statistics; Statistics; Semiparametric model; Semiparametric regression; Mathematics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05395436,0.001279821,0.002234176,0.003443488,0.001017433,0.002755778,0.004062187,0.002643699,0.002679049],"category_scores_gemma":[0.1635781,0.001012006,0.002727503,0.004481295,0.003259244,0.003554625,0.003774287,0.005213345,0.0006066247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001636022,"about_ca_system_score_gemma":0.003455981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00568257,"about_ca_topic_score_gemma":0.004905194,"domain_scores_codex":[0.9555868,0.03819821,0.000823773,0.002108943,0.002839938,0.0004424457],"domain_scores_gemma":[0.8747442,0.1104127,0.004413411,0.007802404,0.002053,0.0005741859],"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":[0.0001772833,0.0001548802,0.00914659,0.0006102988,0.0007862982,0.000360403,0.0006739456,0.2274628,0.0004449539,0.5968341,0.003393203,0.1599553],"study_design_scores_gemma":[0.00007785734,0.00008693188,0.001258294,0.0001329614,0.0001294898,0.0001530072,0.00008572316,0.4550846,0.0002873488,0.5381113,0.004544416,0.00004818439],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001676788,0.0004291894,0.9968321,0.0004480474,0.00004315362,0.00006734014,0.00006393251,0.00009275036,0.000346765],"genre_scores_gemma":[0.1704888,0.001943352,0.8228903,0.000754933,0.0003986779,0.001349534,0.0003652117,0.0001324527,0.001676769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05395436,"threshold_uncertainty_score":0.2853413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4949150392894701,"score_gpt":0.4940527315459541,"score_spread":0.0008623077435159221,"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."}}