{"id":"W2750960820","doi":"10.1016/j.jeconom.2017.08.002","title":"Double instrumental variable estimation of interaction models with big data","year":2017,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Instrumental variable; Principal component analysis; Mathematics; Matrix (chemical analysis); Data Matrix; Variable (mathematics); Algorithm; Statistics; Applied mathematics; Computer science; Mathematical analysis","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":[0.0008419921,0.00005942213,0.0001606498,0.0005948878,0.00008233298,0.0003588059,0.00136931,0.00003564777,0.000005231509],"category_scores_gemma":[0.00008801555,0.00004969152,0.00002375264,0.0002824621,0.00002571808,0.005781951,0.0002768702,0.0001315125,0.000001512028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005897848,"about_ca_system_score_gemma":0.000135005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003699095,"about_ca_topic_score_gemma":0.000004721303,"domain_scores_codex":[0.9992641,0.00001531499,0.0003708134,0.0001145369,0.0001644173,0.00007077152],"domain_scores_gemma":[0.9976651,0.00004246481,0.001336298,0.0007481225,0.0001621982,0.00004580001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003098286,0.0007869552,0.006141453,0.00008936222,0.000295709,0.00001432864,0.001336433,0.1744871,0.0002673941,0.2835567,0.002672448,0.5300423],"study_design_scores_gemma":[0.001253941,0.0004897079,0.001303926,0.00007600411,0.00002113622,0.0001319516,0.00004731555,0.9745382,0.004717302,0.01562471,0.001661147,0.0001346266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0608887,0.00002526889,0.933305,0.0002466613,0.0003299806,0.00005459145,0.000004044171,0.00001058335,0.005135197],"genre_scores_gemma":[0.7532698,0.00002934893,0.2466066,0.00002646213,0.00003665989,4.704228e-7,0.000002237214,0.000003245054,0.0000252049],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8000512,"threshold_uncertainty_score":0.4191775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3371485109574406,"score_gpt":0.324466491978484,"score_spread":0.01268201897895654,"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."}}