{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000666517,0.0002249587,0.0004736064,0.005129929,0.00016613,0.00003415357,0.0001791492,0.00004925158,0.000005603625],"category_scores_gemma":[0.003812195,0.0002128884,0.00004405224,0.01658018,0.00004034791,0.0001629791,0.000367791,0.0001493253,0.000002808164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009260611,"about_ca_system_score_gemma":0.00004467998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003384527,"about_ca_topic_score_gemma":0.000006402257,"domain_scores_codex":[0.9983234,0.0001150867,0.0003890773,0.0003712546,0.000470735,0.0003304725],"domain_scores_gemma":[0.9939585,0.005424869,0.0001463191,0.0002681148,0.000101997,0.0001001668],"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":[0.0001536736,0.002927269,0.05499782,0.000650696,0.0003572308,0.0002497906,0.004265597,0.02300282,0.0009735962,0.687804,0.001715747,0.2229017],"study_design_scores_gemma":[0.00155018,0.00295879,0.01111828,0.00007073791,0.00008462423,0.00002519731,0.0005157439,0.03484055,0.001500082,0.9457266,0.0009173425,0.0006918191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9339429,0.002491089,0.06156708,0.0001518136,0.0001737683,0.001116273,0.00002984462,0.0002703312,0.0002568812],"genre_scores_gemma":[0.7752811,0.0001812394,0.2236788,0.00009743234,0.000009694009,0.0006205888,0.000008580881,0.00002688389,0.00009577777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2579226,"threshold_uncertainty_score":0.8681336,"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."}}