{"id":"W3157535463","doi":"10.1002/jae.2819","title":"Counterfactual analysis under partial identification using locally robust refinement","year":2021,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Counterfactual thinking; Robustness (evolution); Computer science; Set (abstract data type); Econometrics; Representation (politics); Focus (optics); Identification (biology); Mathematical optimization; Mathematical economics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001313075,0.0001559009,0.0006024296,0.00110114,0.00009796251,0.0001556289,0.0002033381,0.0001131782,0.002154697],"category_scores_gemma":[0.00005692575,0.0001921882,0.0002935127,0.001183384,0.00004528004,0.0003412656,0.00006347528,0.0001778027,0.0002647678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007416942,"about_ca_system_score_gemma":0.00006071842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001417394,"about_ca_topic_score_gemma":0.000008624073,"domain_scores_codex":[0.9976334,0.00001182634,0.001695563,0.000347017,0.00008125283,0.0002309166],"domain_scores_gemma":[0.9977746,0.0000645049,0.001680547,0.0002969987,0.00006147747,0.0001218604],"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":[0.00004353301,0.0003341395,0.09430376,0.00002019698,0.001554235,0.000006340813,0.0002512724,0.8590315,0.000365167,0.04249941,0.0002975131,0.001292957],"study_design_scores_gemma":[0.004989676,0.0002607943,0.7403271,0.00002372843,0.001645965,0.00007583223,0.003363553,0.1927918,0.007611273,0.03025497,0.01707907,0.001576247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7572491,0.0007395683,0.2374491,0.0001523179,0.0004328245,0.0000697667,0.00004641494,0.000005753918,0.00385515],"genre_scores_gemma":[0.9946913,0.000398534,0.004237546,0.0002597028,0.0001820497,0.000003091936,0.00003776711,0.00002048851,0.0001695244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6662397,"threshold_uncertainty_score":0.9987575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1761055867481138,"score_gpt":0.2377367973759869,"score_spread":0.06163121062787302,"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."}}