{"id":"W2964938859","doi":"10.48550/arxiv.1908.00215","title":"Illusion of Causality in Visualized Data","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Causality (physics); Causation; Illusion; Interpretation (philosophy); Bar chart; Psychology; Causal reasoning; Visualization; Cognitive psychology; Correlation; Cognition; Computer science; Social psychology; Artificial intelligence; Mathematics; Statistics; Epistemology","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0005253426,0.0001810502,0.0003635977,0.0003058944,0.00003001361,0.0000560641,0.003746683,0.0001871682,0.00003919334],"category_scores_gemma":[0.00008229648,0.0002061762,0.00006923504,0.0007635962,0.00006749064,0.0005254807,0.008967021,0.0002617213,0.00005278224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008414849,"about_ca_system_score_gemma":0.0002601065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004146222,"about_ca_topic_score_gemma":0.0001222171,"domain_scores_codex":[0.9982391,0.0001913596,0.0002839937,0.0009767205,0.0001185533,0.0001903009],"domain_scores_gemma":[0.9963571,0.0000772698,0.0003177098,0.003055334,0.0001178124,0.00007470491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005439213,0.0006585559,0.0325359,0.0006042275,0.0001226467,0.0002269318,0.0005264479,0.1489446,0.0001346601,0.8128576,0.002381515,0.0009524713],"study_design_scores_gemma":[0.0005109305,0.00001639793,0.00259962,0.0001204597,0.00002415349,5.672664e-7,0.00003888699,0.9884623,0.00005287625,0.006405232,0.001530001,0.0002385456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09014406,0.00003141644,0.9073116,0.00005856166,0.0004175303,0.0002175077,0.0001879355,0.00008230119,0.00154906],"genre_scores_gemma":[0.9974399,0.0002417077,0.0009587471,0.00008491077,0.00001595017,7.549723e-8,0.0003943204,0.000008667024,0.0008557083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9072958,"threshold_uncertainty_score":0.9990483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1784209683597186,"score_gpt":0.2778278528546118,"score_spread":0.09940688449489321,"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."}}