{"id":"W4391210985","doi":"10.2139/ssrn.4679414","title":"Towards Automating Causal Discovery in Financial Markets and Beyond","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; University of Toronto","funders":"","keywords":"Key (lock); Computer science; Causal model; Data science; Judgement; Causal inference; Causality (physics); Financial market; Artificial intelligence; Risk analysis (engineering); Finance; Economics; Econometrics; Business; Computer security","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":["metaresearch","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.02891144,0.0002076,0.0003354498,0.0006812628,0.0002062805,0.001037187,0.0005053709,0.0001164233,0.00007272577],"category_scores_gemma":[0.01012434,0.0001539618,0.0001328948,0.001122268,0.00009116479,0.0009654194,0.0001959165,0.002229746,0.00002238594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007163701,"about_ca_system_score_gemma":0.004137197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004110658,"about_ca_topic_score_gemma":0.0006708086,"domain_scores_codex":[0.9950129,0.0007037224,0.0007755949,0.0005067994,0.001069243,0.00193173],"domain_scores_gemma":[0.9974014,0.002020905,0.0001633489,0.0002284905,0.00008004288,0.0001057696],"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":[0.00007073308,0.00001648812,0.004926504,0.000007569078,0.00002409245,0.00007563592,0.0004404822,0.0000178834,0.000156723,0.06849276,0.0006525848,0.9251186],"study_design_scores_gemma":[0.0003606088,0.0001609889,0.05144,0.00008940411,0.00001598158,0.001912199,0.0008722374,0.009069468,0.00004084434,0.9334358,0.002380069,0.0002223914],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9214036,0.006683046,0.05968206,0.001412705,0.00144975,0.000132309,0.000005652154,0.00006365949,0.009167217],"genre_scores_gemma":[0.9923752,0.0004095956,0.003139359,0.00008884241,0.0004570099,0.000005908013,5.619909e-7,0.00002652435,0.003497031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9248962,"threshold_uncertainty_score":0.9999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03070707259379107,"score_gpt":0.3601219447481264,"score_spread":0.3294148721543353,"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."}}