{"id":"W4377138740","doi":"10.1002/pds.5639","title":"Differences in target estimands between different propensity score‐based weights","year":2023,"lang":"en","type":"article","venue":"Pharmacoepidemiology and Drug Safety","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care","keywords":"Propensity score matching; Weighting; Statistic; Average treatment effect; Statistics; Mathematics; Matching (statistics); Observational study; Inverse probability weighting; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002617218,0.0003600748,0.001039449,0.0002538809,0.0001901171,0.0000098684,0.0002752098,0.0001737459,0.0001037947],"category_scores_gemma":[0.001613204,0.0002614148,0.00008732593,0.000316458,0.0003181902,0.0001577552,0.0002148057,0.0006030415,0.00002403578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006321046,"about_ca_system_score_gemma":0.00003895838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005026556,"about_ca_topic_score_gemma":0.00005458921,"domain_scores_codex":[0.996788,0.001124721,0.0007538416,0.0005628521,0.0001490296,0.0006215282],"domain_scores_gemma":[0.9912821,0.00797781,0.0002298882,0.0002828494,0.00004747554,0.0001798724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001358394,0.00007172755,0.9782926,0.0001210544,0.00003382086,0.00002383444,0.0001526064,0.000008543445,0.0002142533,0.01657059,0.00213289,0.002242263],"study_design_scores_gemma":[0.0007764822,0.00006510271,0.5491187,0.0001209143,0.00004547438,0.000002536355,0.00001531488,0.00560261,0.004501362,0.4389878,0.000411293,0.0003524392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9566159,0.0001088781,0.03834994,0.002933607,0.0001404746,0.0005874093,0.00005023201,0.0007190207,0.0004945031],"genre_scores_gemma":[0.9898543,0.0003021047,0.008963675,0.0003963695,0.0001100758,0.0001103034,0.00007165218,0.00002696459,0.0001645343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4291739,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2828403345970821,"score_gpt":0.4318202581740272,"score_spread":0.1489799235769452,"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."}}