{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353374,0.0001337023,0.0003909816,0.0002005441,0.00009345216,0.00005368648,0.0001337593,0.00007547336,0.0001236811],"category_scores_gemma":[0.002995319,0.0000793287,0.00005885958,0.0001646109,0.0001385079,0.0001932717,0.0000300659,0.0004501044,0.00002839323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000390852,"about_ca_system_score_gemma":0.0002785161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002303499,"about_ca_topic_score_gemma":0.00004017779,"domain_scores_codex":[0.9981269,0.0002662962,0.0003356124,0.0001671926,0.0007477311,0.0003563072],"domain_scores_gemma":[0.9910959,0.007431467,0.0001789029,0.0001935096,0.0009040341,0.0001961567],"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":[0.0115878,0.003310839,0.010317,0.00202606,0.0007058625,0.001488518,0.001513235,0.00008538691,0.01971413,0.7817132,0.05662842,0.1109095],"study_design_scores_gemma":[0.003233057,0.02626019,0.002895,0.0007728108,0.00009118767,0.0002449763,0.0001712498,0.003389874,0.06877294,0.8908075,0.003069632,0.000291549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3908446,0.00001270013,0.6059618,0.0001265875,0.0001852317,0.001528408,0.00002308621,0.00003228915,0.001285346],"genre_scores_gemma":[0.8023481,0.00005926613,0.195108,0.00001751358,0.0001235216,0.00004609864,0.000004421786,0.00003392541,0.002259053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4115036,"threshold_uncertainty_score":0.3585893,"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."}}