{"id":"W4396659425","doi":"10.1038/s41598-024-60348-4","title":"Causal impact evaluation of occupational safety policies on firms’ default using machine learning uplift modelling","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Gradient boosting; Computer science; Causality (physics); Context (archaeology); Machine learning; Boosting (machine learning); Artificial intelligence; Psychological intervention; Work (physics); Business; Operations research; Medicine; Engineering; Nursing","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":[0.01243011,0.000665559,0.001323939,0.00170914,0.0004556235,0.001457649,0.001137286,0.001374979,0.004798975],"category_scores_gemma":[0.02547494,0.0002157856,0.00196911,0.0010546,0.0008537703,0.0008994674,0.001152368,0.001339155,0.0002189113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331021,"about_ca_system_score_gemma":0.001625728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009540736,"about_ca_topic_score_gemma":0.004955102,"domain_scores_codex":[0.9967335,0.002410955,0.0001345761,0.0002421068,0.0002384026,0.0002404177],"domain_scores_gemma":[0.9537304,0.0424744,0.00162904,0.001000748,0.0008216319,0.0003438203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006069573,0.0004445378,0.05883401,0.0002679024,0.000435036,0.0002390664,0.0001581304,0.8824431,0.0003862724,0.01662596,0.001019886,0.03853914],"study_design_scores_gemma":[0.00002438336,0.0001499388,0.008660348,0.00004081475,0.0001436293,0.00002091212,0.00006431357,0.9807342,0.0003556849,0.009393002,0.0004000769,0.00001278258],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8127153,0.001346358,0.1778753,0.001859293,0.0001341989,0.0002696559,0.0009755032,0.0004447003,0.004379762],"genre_scores_gemma":[0.9911171,0.0002188673,0.007583253,0.00003389849,0.00003064115,0.00005571046,0.0002728879,0.000008069716,0.0006795765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01243011,"threshold_uncertainty_score":0.06573755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3062891477036018,"score_gpt":0.4932595489374078,"score_spread":0.186970401233806,"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."}}