{"id":"W3196666600","doi":"10.48550/arxiv.2109.03757","title":"Causal Inference for Quantile Treatment Effects","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données","keywords":"Quantile; Estimator; Causal inference; Quantile regression; Statistics; Inference; Econometrics; Population; Average treatment effect; Mathematics; Computer science; Artificial intelligence; Medicine; Environmental health","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04139901,0.0008955316,0.001480566,0.002879009,0.001015737,0.002103453,0.002631346,0.002345639,0.01081834],"category_scores_gemma":[0.1721406,0.0006959852,0.002127468,0.003653728,0.003072633,0.003688368,0.002625133,0.003864619,0.0005534653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001886873,"about_ca_system_score_gemma":0.002003923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004873884,"about_ca_topic_score_gemma":0.002866808,"domain_scores_codex":[0.9796588,0.01599667,0.0004923274,0.001980303,0.001396751,0.0004750809],"domain_scores_gemma":[0.8638685,0.1205665,0.005621389,0.007297726,0.002228887,0.0004169269],"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":[0.0001119152,0.0001068045,0.009860401,0.0003067558,0.0005026328,0.0001697841,0.0004434674,0.08222051,0.000329278,0.8032789,0.002210631,0.1004589],"study_design_scores_gemma":[0.00008393346,0.00005110897,0.002473724,0.0000946355,0.0001226426,0.00006675008,0.00007115274,0.2468922,0.0003839473,0.7468587,0.002878589,0.00002261159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007219283,0.0004644635,0.9897515,0.000740137,0.00007083411,0.00009548184,0.0002013468,0.0001928635,0.001264049],"genre_scores_gemma":[0.5380637,0.001948955,0.4508794,0.001037464,0.0004981633,0.001384889,0.0009092098,0.0001730325,0.005105183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04139901,"threshold_uncertainty_score":0.2189415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2776922142114546,"score_gpt":0.3254229962819759,"score_spread":0.04773078207052128,"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."}}