{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004876349,0.0001890223,0.000243871,0.0003944738,0.0002542901,0.0001693718,0.00009123394,0.0000859082,0.0002153207],"category_scores_gemma":[0.001098282,0.0001555492,0.0001391975,0.0005819921,0.0001429091,0.0003284274,0.00006155077,0.0002484284,0.00000573637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003933103,"about_ca_system_score_gemma":0.0005032225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002131059,"about_ca_topic_score_gemma":0.00002818513,"domain_scores_codex":[0.9970468,0.0001210294,0.0007226702,0.0005083265,0.001335501,0.0002656953],"domain_scores_gemma":[0.9982198,0.0002933817,0.0003724476,0.0004964495,0.0005503528,0.00006755507],"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":[0.0000494396,0.00009310332,0.002488469,0.0001405025,0.00006966532,0.00005198952,0.00131224,0.9469856,0.03536089,0.0107525,0.0005692662,0.002126347],"study_design_scores_gemma":[0.00004487706,0.00004136792,0.0001254669,0.0002838605,0.00007351051,0.00009182129,0.00004134755,0.7527714,0.01861989,0.2274837,0.0002742509,0.0001483978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7830626,0.0001851377,0.214559,0.0000135633,0.0009192946,0.0003619691,0.00001541609,0.0002709691,0.0006120673],"genre_scores_gemma":[0.9825636,0.000003627828,0.01692617,0.00000334811,0.00005614345,0.00001283563,0.0000880652,0.00003193146,0.000314267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2167313,"threshold_uncertainty_score":0.6343111,"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."}}