{"id":"W3143332811","doi":"10.1177/1536867x211000008","title":"msreg: A command for consistent estimation of linear regression models using matched data","year":2021,"lang":"en","type":"article","venue":"The Stata Journal Promoting communications on statistics and Stata","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Ordinary least squares; Matching (statistics); Statistics; Econometrics; Sample (material); Parametric statistics; Linear regression; Least-squares function approximation; Regression; Mathematics; Computer science","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":[0.002668425,0.000185356,0.0004226289,0.00006197044,0.0009791668,0.00015874,0.001034349,0.00005151335,0.00001801407],"category_scores_gemma":[0.006807721,0.0001294449,0.00003867209,0.000155246,0.0003264968,0.0001612657,0.0008790537,0.0004445093,4.25339e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003510805,"about_ca_system_score_gemma":0.0002461029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002624296,"about_ca_topic_score_gemma":0.00002010115,"domain_scores_codex":[0.9975716,0.0007350937,0.0008795047,0.0002434455,0.0003307811,0.0002394998],"domain_scores_gemma":[0.9872379,0.00884199,0.0007473842,0.002363795,0.0006938027,0.000115096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001284091,0.0005151091,0.00002494053,0.0005176136,0.000223703,0.000009723805,0.002993932,0.0007665122,0.0004911138,0.8981333,0.002968539,0.09322713],"study_design_scores_gemma":[0.0003755892,0.0000852958,0.00001497894,0.0003512914,0.0001336273,0.00005598179,0.0005757016,0.5731102,0.00009180122,0.4249641,0.000151432,0.00009002851],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004999455,0.0005427432,0.9870858,0.001154353,0.00006694265,0.0003668195,0.005653911,0.00001394387,0.0001159901],"genre_scores_gemma":[0.1121787,0.0008059955,0.8865254,0.00006127793,0.00002182414,0.00000776812,0.0003391742,0.00002887017,0.00003101499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5723437,"threshold_uncertainty_score":0.814997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4693799478599424,"score_gpt":0.4955069563491542,"score_spread":0.02612700848921179,"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."}}