{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002956675,0.0003922057,0.0007734859,0.0002097956,0.0000535677,0.00002165739,0.0004019014,0.0003313965,0.00001878191],"category_scores_gemma":[0.0004849093,0.0003727993,0.0001220769,0.0002184721,0.0003587054,0.0002183602,0.001033404,0.0005574981,0.000007492682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001658541,"about_ca_system_score_gemma":0.0001007621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002424635,"about_ca_topic_score_gemma":0.0001873126,"domain_scores_codex":[0.9983255,0.0001664279,0.0003109915,0.0008097997,0.0001103218,0.0002769324],"domain_scores_gemma":[0.9967197,0.001300112,0.0004801965,0.001179154,0.0002128178,0.0001079804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003929328,0.0004916782,0.8916348,0.007479078,0.00037822,0.0004363767,0.0004329595,0.007712707,0.002108945,0.08872321,0.0001306661,0.00007842814],"study_design_scores_gemma":[0.002762276,0.001054046,0.08899066,0.002115753,0.001268605,0.00002215244,0.0004743769,0.02111366,0.01921579,0.8609558,0.0000489738,0.001977886],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9106993,0.00002926487,0.08756695,0.000007466037,0.0001105822,0.0009644249,0.00004997773,0.0002734934,0.0002985455],"genre_scores_gemma":[0.9938232,0.0000478505,0.005417163,0.00001076227,0.00001468364,0.000003977155,0.0000116725,0.00004449156,0.0006262057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8026441,"threshold_uncertainty_score":0.9998724,"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."}}