{"id":"W4399828294","doi":"10.32920/26052673.v1","title":"Applications of Causal Inference","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Inference; Causal inference; Computer science; Artificial intelligence; Econometrics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0315589,0.001861983,0.002144554,0.009475785,0.003424262,0.007588211,0.003865408,0.004224066,0.01560582],"category_scores_gemma":[0.1167119,0.001550795,0.003835835,0.007157118,0.01372147,0.01362995,0.008966312,0.008500923,0.002305536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005689356,"about_ca_system_score_gemma":0.005483678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005054818,"about_ca_topic_score_gemma":0.003125444,"domain_scores_codex":[0.9694922,0.02045078,0.001416257,0.003680556,0.004524127,0.0004361888],"domain_scores_gemma":[0.8747352,0.1064946,0.002997682,0.01160428,0.003458231,0.0007100475],"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":[0.00001056546,0.00001555103,0.0005131909,0.0001379111,0.00005270734,0.00007197757,0.000318433,0.004404452,0.00006530691,0.9692258,0.001597706,0.0235862],"study_design_scores_gemma":[0.000009063129,0.000004765355,0.00008216904,0.00009364125,0.0000100251,0.00005087188,0.00006484534,0.00931485,0.0001147208,0.9801587,0.01008594,0.00001038089],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001492625,0.002269981,0.9677275,0.006979405,0.0002829191,0.0001169439,0.0002679738,0.0003632435,0.02049939],"genre_scores_gemma":[0.2246279,0.006436954,0.753551,0.003612058,0.001727667,0.0007920092,0.000766687,0.0005142567,0.007971408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0315589,"threshold_uncertainty_score":0.1669014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187327467075799,"score_gpt":0.3138015278462048,"score_spread":0.2819282531754468,"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."}}