{"id":"W4408028303","doi":"10.2139/ssrn.5146292","title":"Improving Targeting Policies Using Transfer Learning","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Transfer of learning; Policy learning; Computer science; Artificial intelligence; Machine learning","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":[0.005302547,0.001388076,0.002160326,0.001510304,0.0008675999,0.001847039,0.001912882,0.003655434,0.008520261],"category_scores_gemma":[0.04186757,0.0009543957,0.0009414653,0.001482344,0.001499881,0.005047018,0.002730432,0.004667337,0.001632592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478509,"about_ca_system_score_gemma":0.002541019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993432,"about_ca_topic_score_gemma":0.001932483,"domain_scores_codex":[0.9973806,0.001423422,0.0000968561,0.0004869281,0.0003449398,0.0002672052],"domain_scores_gemma":[0.9723879,0.02342131,0.001096524,0.00172075,0.0009368164,0.0004366657],"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":[0.0004657671,0.0004125102,0.002181719,0.0002540064,0.0001574533,0.0001274573,0.0001643592,0.7232966,0.001962955,0.1068135,0.007655144,0.1565085],"study_design_scores_gemma":[0.00002932877,0.00003305299,0.0001152216,0.00001231256,0.000018036,0.0000117681,0.00001032711,0.9256904,0.0005257567,0.07320445,0.0003424414,0.00000691462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02681275,0.0003683428,0.9661191,0.001288854,0.00012148,0.00006271415,0.0001143919,0.001096662,0.004015756],"genre_scores_gemma":[0.8668212,0.000502724,0.1228381,0.0006293193,0.0003131604,0.0001866722,0.0002964065,0.0003015925,0.008110843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008520261,"threshold_uncertainty_score":0.02850318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08629939527334263,"score_gpt":0.3893813575189802,"score_spread":0.3030819622456375,"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."}}