{"id":"W4380051600","doi":"10.1007/s00181-023-02441-7","title":"When to use matching and weighting or regression in instrumental variable estimation? Evidence from college proximity and returns to college","year":2023,"lang":"en","type":"article","venue":"Empirical Economics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut für Arbeitsmarkt- und Berufsforschung","keywords":"Instrumental variable; Covariate; Econometrics; Propensity score matching; Weighting; Estimator; Matching (statistics); Average treatment effect; Statistics; Variance (accounting); Regression; Economics; Estimation; Variance inflation factor; Linear regression; Mathematics; Multicollinearity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1177309,0.001031971,0.002692827,0.002612176,0.001097957,0.003805107,0.003627833,0.003036979,0.004515743],"category_scores_gemma":[0.3552552,0.0008476292,0.00192311,0.006076247,0.004297037,0.00570574,0.003692458,0.004016467,0.001385477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008672428,"about_ca_system_score_gemma":0.001729912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004562594,"about_ca_topic_score_gemma":0.003121015,"domain_scores_codex":[0.8881563,0.09808995,0.004144427,0.003570024,0.004894631,0.00114465],"domain_scores_gemma":[0.7790524,0.164072,0.01750717,0.03214857,0.006218004,0.001001838],"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":[0.001015387,0.0005443326,0.09522714,0.001231699,0.003583808,0.0002934959,0.001995281,0.02634647,0.00160501,0.2734842,0.02167031,0.5730029],"study_design_scores_gemma":[0.00109601,0.0006349357,0.02896021,0.002004182,0.001093067,0.0002391547,0.001793884,0.1456542,0.006985832,0.7750996,0.0361767,0.0002621842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07475849,0.007237301,0.8771371,0.03047453,0.001385486,0.0006236103,0.0004534971,0.0004857233,0.007444292],"genre_scores_gemma":[0.6197277,0.003306959,0.3639299,0.006704775,0.001264205,0.000850982,0.0005243756,0.0002785533,0.003412511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1177309,"threshold_uncertainty_score":0.622628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2113409460094679,"score_gpt":0.4144630865748464,"score_spread":0.2031221405653786,"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."}}