{"id":"W3082894717","doi":"10.1002/sim.8722","title":"Multiply robust estimation of causal quantile treatment effects","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Roche (Canada)","funders":"","keywords":"Quantile; Causal inference; Estimator; Propensity score matching; Econometrics; Statistics; Inverse probability weighting; Empirical likelihood; Weighting; Average treatment effect; Inference; Computer science; Estimation; Mathematics; Artificial intelligence; Medicine; Economics","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":[0.0001678923,0.0001578895,0.0004494474,0.00007453049,0.00001839943,0.000002523363,0.00009282758,0.00005560828,0.00007878277],"category_scores_gemma":[0.004643891,0.000124109,0.00001526623,0.0001839804,0.000144519,0.00004477447,0.00002386967,0.00011673,0.000005808932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008980677,"about_ca_system_score_gemma":0.00003411687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009741008,"about_ca_topic_score_gemma":0.00005221751,"domain_scores_codex":[0.9989106,0.00006624249,0.0004426321,0.0001794728,0.0002404974,0.0001605516],"domain_scores_gemma":[0.9976616,0.001819035,0.0001815364,0.0001835429,0.00007262835,0.00008165655],"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":[0.000192349,0.0004536992,0.001882417,0.002019781,0.00008911126,0.0002892744,0.01024355,0.003810558,0.01326799,0.8748778,0.01233745,0.08053602],"study_design_scores_gemma":[0.003835686,0.005103339,0.0018121,0.0009502524,0.00020288,0.000007568421,0.0005647121,0.2677565,0.03662734,0.6825323,0.0002116843,0.0003956799],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0145556,0.00005924012,0.9838831,0.000335062,0.00007275768,0.0004726692,0.00006553411,0.0001079998,0.0004479914],"genre_scores_gemma":[0.4549971,0.00004720179,0.544736,0.00007122596,0.00004247449,0.00002960211,0.00003628318,0.00001721452,0.00002289258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4404415,"threshold_uncertainty_score":0.5559507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1383377641491244,"score_gpt":0.4358404304734299,"score_spread":0.2975026663243055,"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."}}