{"id":"W3129669709","doi":"10.48550/arxiv.2111.06486","title":"Variational Auto-Encoder Architectures that Excel at Causal Inference","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Causal inference; Inference; Discriminative model; Generative grammar; Computer science; Artificial intelligence; Machine learning; Generative model; Observational study; Sequence (biology); Causal model; Series (stratigraphy); Econometrics; 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":[0.00382074,0.001137764,0.00134677,0.001071653,0.00054749,0.001356094,0.002602107,0.001625273,0.004323746],"category_scores_gemma":[0.01528081,0.001100496,0.001304444,0.001198972,0.001400564,0.002827231,0.002478573,0.003717348,0.001028874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321997,"about_ca_system_score_gemma":0.001669463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007047489,"about_ca_topic_score_gemma":0.01335389,"domain_scores_codex":[0.9983721,0.0009438816,0.00006304224,0.0003346888,0.0002010478,0.00008522231],"domain_scores_gemma":[0.9918372,0.006561494,0.0003477456,0.0007393091,0.0003651908,0.0001490651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001285067,0.0001187225,0.003259914,0.0001921714,0.0002807541,0.0001234397,0.0002343731,0.5916507,0.001384533,0.2574061,0.004595678,0.1406251],"study_design_scores_gemma":[0.000008236705,0.00001153012,0.0001497004,0.00001669156,0.00001692988,0.00002304571,0.000006808973,0.9364443,0.0002202801,0.0623338,0.0007609015,0.000007707767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006026076,0.0006187703,0.9907628,0.0004094314,0.0000424517,0.00002574878,0.0001438851,0.000422387,0.001548474],"genre_scores_gemma":[0.5134983,0.0017326,0.4735548,0.0007591223,0.0003208244,0.0002380205,0.001293002,0.0004666325,0.00813669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007047489,"threshold_uncertainty_score":0.02020621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2576327158538822,"score_gpt":0.295841922690196,"score_spread":0.03820920683631379,"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."}}