{"id":"W4386937233","doi":"10.54254/2755-2721/6/20230930","title":"Multi-treatment casual analysis using improved meta learner and uplift tree","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Causal inference; Causality (physics); Computer science; Inference; Machine learning; Artificial intelligence; Focus (optics); Casual; Tree (set theory); Causal model; Process (computing); Econometrics; Mathematics; Statistics","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":[0.00008367411,0.0001463635,0.0002656992,0.000186168,0.00005693294,0.00002831068,0.0000316068,0.00004043074,0.00000511066],"category_scores_gemma":[0.00001751903,0.0001271216,0.00005436409,0.0003285189,0.00001894853,0.00004793626,0.00004473454,0.00005556488,0.000001300026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002974402,"about_ca_system_score_gemma":0.000009701294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001444551,"about_ca_topic_score_gemma":0.000005905466,"domain_scores_codex":[0.999421,0.000004759532,0.0001515449,0.0001958756,0.00008466521,0.0001421633],"domain_scores_gemma":[0.9995497,0.0002593538,0.00003921071,0.00007654412,0.00002272714,0.00005240082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001040659,0.00006098881,0.0001560581,0.00007407137,0.003504178,0.00001100389,0.0007392783,0.865293,0.02650345,0.09394309,0.000007052042,0.009697434],"study_design_scores_gemma":[0.0002271993,0.00001773992,0.001109974,0.000003336154,0.0007775658,0.000003615925,0.00006051396,0.9841405,0.0005602782,0.01292652,0.00002530704,0.0001474522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2833876,0.00006109963,0.7160286,0.00001358321,0.000009216983,0.0001352317,0.000007176672,0.0003261732,0.00003135422],"genre_scores_gemma":[0.7247898,0.00001170553,0.2750742,0.000008216694,0.00001420881,0.00003909008,0.00001701129,0.00001555712,0.00003021754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4414023,"threshold_uncertainty_score":0.5183868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1468626078979809,"score_gpt":0.3694765840489,"score_spread":0.2226139761509191,"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."}}