{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01048366,0.0006689057,0.000458919,0.002360977,0.0007290279,0.004043184,0.0006851354,0.001324896,0.004854714],"category_scores_gemma":[0.1058029,0.0006704626,0.0008335201,0.001305614,0.002668366,0.007128066,0.004212193,0.001594391,0.000322998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009423542,"about_ca_system_score_gemma":0.0005420395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009527399,"about_ca_topic_score_gemma":0.001007098,"domain_scores_codex":[0.9876449,0.007682188,0.0008979311,0.001319587,0.002182303,0.0002731217],"domain_scores_gemma":[0.8521532,0.1153996,0.01197289,0.01399274,0.005430657,0.001050951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.006637364,0.0004110885,0.1637436,0.009779828,0.001148842,0.003792219,0.1955443,0.02082743,0.1340326,0.1139092,0.01587483,0.3342987],"study_design_scores_gemma":[0.0007770701,0.001848846,0.2680015,0.005008405,0.001259381,0.009454142,0.06501041,0.1071004,0.07155871,0.3452623,0.1234933,0.001225531],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7574465,0.002561483,0.2129546,0.004162677,0.0003247514,0.0002636851,0.00195213,0.002682871,0.01765115],"genre_scores_gemma":[0.951914,0.000375347,0.04607286,0.0003253091,0.00006656961,0.00008220343,0.0003808414,0.0002252468,0.000557457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01048366,"threshold_uncertainty_score":0.05544353,"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."}}