{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01010657,0.001410805,0.002212906,0.004766221,0.001249474,0.004637673,0.002746264,0.002342043,0.004360637],"category_scores_gemma":[0.05214504,0.001227572,0.002843967,0.00285959,0.001437083,0.00490125,0.004609937,0.003753134,0.001843899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007735511,"about_ca_system_score_gemma":0.00408029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005333103,"about_ca_topic_score_gemma":0.007842164,"domain_scores_codex":[0.9941589,0.003144781,0.0004477276,0.0009763688,0.001057081,0.0002150783],"domain_scores_gemma":[0.931846,0.05804094,0.001592483,0.006029755,0.001939432,0.0005513524],"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":[0.0004771323,0.0004854877,0.01286575,0.001112988,0.0009139678,0.0005187663,0.000704399,0.09844995,0.009885482,0.1413076,0.00926548,0.7240131],"study_design_scores_gemma":[0.00005170616,0.00004466678,0.0009961535,0.00007054559,0.0001483289,0.0001394841,0.0001065153,0.6847623,0.003048417,0.3057888,0.004817745,0.00002547994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005262085,0.0006110445,0.9906902,0.0007307793,0.00005714328,0.0000589536,0.0002443428,0.001843662,0.000501813],"genre_scores_gemma":[0.1435689,0.001093962,0.8514772,0.0005894756,0.0001997327,0.0001661407,0.001038587,0.000206227,0.001659781],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01010657,"threshold_uncertainty_score":0.05344933,"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."}}