{"id":"W4409364512","doi":"10.1609/aaai.v39i16.33808","title":"Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Ambiguity; Robust optimization; Mathematical optimization; Computer science; Optimal design; Operations research; Mathematics; 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":[0.00707095,0.001675354,0.001936523,0.001498523,0.0007694414,0.002137691,0.001893325,0.002692326,0.003354861],"category_scores_gemma":[0.02495873,0.00117998,0.001649957,0.001024363,0.00284228,0.004179643,0.005005165,0.004649471,0.0005525081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001878225,"about_ca_system_score_gemma":0.002474264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001502726,"about_ca_topic_score_gemma":0.001336538,"domain_scores_codex":[0.9976185,0.001162737,0.0001446057,0.0004266441,0.0004910248,0.0001564712],"domain_scores_gemma":[0.9906448,0.007105454,0.0007253772,0.0006960118,0.0005893076,0.0002390373],"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":[0.00006352233,0.0000526809,0.0004714598,0.0001117026,0.00004945445,0.00009675825,0.00009693318,0.7971911,0.001302776,0.1717325,0.00131241,0.02751871],"study_design_scores_gemma":[0.000008780427,0.00002118125,0.00004281229,0.00001526502,0.00000527321,0.00001623667,0.000009617476,0.9415953,0.0004385879,0.0573121,0.0005255158,0.000009482613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002974986,0.00006062347,0.9960305,0.0001949251,0.00001327166,0.00002430728,0.00002269853,0.00008479375,0.0005938656],"genre_scores_gemma":[0.4111617,0.0005195411,0.5823868,0.0005837599,0.000138264,0.0004735951,0.0003721824,0.0004697241,0.003894371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00707095,"threshold_uncertainty_score":0.03739518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2070059877216393,"score_gpt":0.4028289779424902,"score_spread":0.1958229902208509,"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."}}