{"id":"W3085668815","doi":"10.47302/jsr.2018520205","title":"Bootstrap bias correction for average treatment effects with inverse propensity weights","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Memorial University of Newfoundland","funders":"","keywords":"Propensity score matching; Estimator; Average treatment effect; Endogeneity; Observational study; Econometrics; Statistics; Mathematics; Instrumental variable; Confounding; Inverse; Treatment effect; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.04625896,0.0006613208,0.001389642,0.002343926,0.0005535616,0.001279008,0.002107147,0.001534997,0.003951716],"category_scores_gemma":[0.1870849,0.000548039,0.001625454,0.002538694,0.00143541,0.001265031,0.002025121,0.002188927,0.0008639556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007344637,"about_ca_system_score_gemma":0.001766191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00263808,"about_ca_topic_score_gemma":0.001820078,"domain_scores_codex":[0.9691725,0.02423309,0.001047745,0.00171525,0.003308885,0.0005223934],"domain_scores_gemma":[0.8996779,0.07646418,0.005840808,0.01232322,0.005259146,0.0004348322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007818386,0.0002391118,0.02957261,0.001386514,0.001836905,0.0006756183,0.0009206557,0.1150545,0.004930493,0.241225,0.01022816,0.5931485],"study_design_scores_gemma":[0.000351379,0.0003808068,0.01633031,0.000691518,0.0006051423,0.0007546103,0.0002225099,0.6541256,0.009537914,0.2909219,0.02596393,0.0001144877],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006149596,0.0004929322,0.991739,0.0002334939,0.0001161379,0.0001471412,0.0000981264,0.0002714555,0.0007521156],"genre_scores_gemma":[0.28664,0.0009061812,0.7068195,0.0007317259,0.0003397143,0.001163387,0.000563359,0.0002964131,0.002539638],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04625896,"threshold_uncertainty_score":0.2446437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4277692981758229,"score_gpt":0.5138247682271289,"score_spread":0.08605547005130593,"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."}}