{"id":"W3109567526","doi":"10.48550/arxiv.1907.13323","title":"Identifiability of causal effects with multiple causes and a binary outcome","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Confounding; Outcome (game theory); Causal inference; Identifiability; Econometrics; Observational study; Inference; Latent variable; Probit; Probit model; Instrumental variable; Causal model; Statistics; Computer science; Mathematics; Artificial intelligence","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":[0.1095603,0.001647291,0.005456148,0.004030755,0.002235648,0.004654081,0.005000785,0.006137324,0.007455144],"category_scores_gemma":[0.2703473,0.002046512,0.005142443,0.005263725,0.009345246,0.008483278,0.007946154,0.009730745,0.001048615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00299398,"about_ca_system_score_gemma":0.005663358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003770128,"about_ca_topic_score_gemma":0.002128317,"domain_scores_codex":[0.92456,0.05172091,0.003783888,0.01083562,0.007818656,0.001280847],"domain_scores_gemma":[0.6282755,0.3231707,0.0190473,0.02571326,0.003121727,0.0006715813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001203275,0.0001019965,0.007003089,0.0008282063,0.0006537739,0.0004801967,0.0009067365,0.01022048,0.0003076786,0.9396698,0.001457874,0.03824979],"study_design_scores_gemma":[0.00005678778,0.0000358073,0.001038068,0.0001470595,0.0001358171,0.0002066614,0.00008245578,0.02117972,0.000268243,0.9748393,0.001983341,0.00002674528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006858023,0.000951877,0.9868612,0.002790158,0.0001204718,0.0002135488,0.0003301161,0.0001210458,0.001753522],"genre_scores_gemma":[0.426164,0.002835489,0.5585245,0.002763276,0.0009022366,0.002141907,0.001205304,0.0001671933,0.005296004],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1095603,"threshold_uncertainty_score":0.579417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1738797619208685,"score_gpt":0.2878945172744816,"score_spread":0.1140147553536131,"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."}}