{"id":"W4321611071","doi":"10.1093/biostatistics/kxad002","title":"Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations?","year":2023,"lang":"en","type":"review","venue":"Biostatistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute on Minority Health and Health Disparities; National Institute of Environmental Health Sciences; Wellcome Trust; Alfred P. Sloan Foundation; National Institute on Aging; National Institutes of Health; Harvard University","keywords":"Causal inference; Estimator; Context (archaeology); Computer science; Time series; Inference; Econometrics; Series (stratigraphy); Nonparametric statistics; Statistics; Machine learning; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009248536,0.0003550107,0.001222181,0.0002878563,0.00005598766,0.0001067794,0.0003519673,0.0002008351,0.00000233391],"category_scores_gemma":[0.007827202,0.000276519,0.00006508705,0.0005452819,0.0001456277,0.0003389577,0.0005557932,0.000341358,0.000002885052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002071559,"about_ca_system_score_gemma":0.000093972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004939621,"about_ca_topic_score_gemma":0.0001580922,"domain_scores_codex":[0.9976269,0.0006088704,0.0008823143,0.0004110743,0.00022484,0.0002460172],"domain_scores_gemma":[0.9925236,0.006207733,0.0006359481,0.0005285965,0.00007268015,0.00003151571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00001417557,0.000580884,0.0005573265,0.2573318,0.0004167431,0.0002909683,0.001304018,0.00001852033,0.00001002774,0.01838445,0.0005126168,0.7205784],"study_design_scores_gemma":[0.001340674,0.0007983744,0.003919378,0.7259555,0.003459052,0.0001416879,0.0006999509,0.004224275,0.00007882481,0.2550743,0.002127679,0.002180232],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003524683,0.6305895,0.3628299,0.000008610722,0.0001905974,0.005201478,0.0006291559,0.0001865554,0.00001166221],"genre_scores_gemma":[0.002594856,0.949761,0.04454511,0.00000773422,0.0001085291,0.001286359,0.001208978,0.0003094996,0.0001779447],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7183982,"threshold_uncertainty_score":0.9999687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1328725503628073,"score_gpt":0.4694060812927,"score_spread":0.3365335309298927,"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."}}