{"id":"W2969737323","doi":"10.1109/tvcg.2019.2934399","title":"Illusion of Causality in Visualized Data","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Causality (physics); Visualization; Computer science; Causation; Illusion; Bar chart; Causal reasoning; Interpretation (philosophy); Data visualization; Cognition; Cognitive psychology; Psychology; Artificial intelligence; Statistics; Mathematics","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":[0.01006516,0.0006960874,0.0004616166,0.002350036,0.0007133489,0.003984517,0.0006687928,0.001349538,0.005124667],"category_scores_gemma":[0.1008671,0.000665054,0.0008247262,0.001239498,0.002600484,0.007021231,0.004297308,0.001619761,0.0003268266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009072899,"about_ca_system_score_gemma":0.0005275506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009094631,"about_ca_topic_score_gemma":0.0009663416,"domain_scores_codex":[0.9880973,0.007427725,0.0008259829,0.001271566,0.002107771,0.0002696866],"domain_scores_gemma":[0.8601738,0.1098015,0.01117831,0.01270019,0.005129484,0.001016698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006714202,0.0004275406,0.1548265,0.01059239,0.001158755,0.003889074,0.2068311,0.02025356,0.1443752,0.1090616,0.01659428,0.3252758],"study_design_scores_gemma":[0.0008075874,0.00198031,0.2706132,0.005560223,0.001311051,0.009871099,0.06973046,0.1030415,0.07020601,0.3365978,0.1289901,0.001290674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7580516,0.00276014,0.2113305,0.004147751,0.0003418453,0.0002747061,0.002004442,0.002733476,0.01835555],"genre_scores_gemma":[0.9512008,0.0004028688,0.04667398,0.0003510233,0.0000705742,0.00008989483,0.0003890687,0.0002386516,0.0005832234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01006516,"threshold_uncertainty_score":0.05323023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745433781171757,"score_gpt":0.3289071690966643,"score_spread":0.2914528312849467,"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."}}