{"id":"W3126063643","doi":"10.1093/ectj/utaa013","title":"Two-way exclusion restrictions in models with heterogeneous treatment effects","year":2020,"lang":"en","type":"article","venue":"Econometrics Journal","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; National Natural Science Foundation of China","keywords":"Outcome (game theory); Estimator; Latent variable; Instrumental variable; Econometrics; Structural equation modeling; Mathematics; Treatment effect; Monotone polygon; Variable (mathematics); Selection (genetic algorithm); Contrast (vision); Statistics; Applied mathematics; Computer science; Medicine; Mathematical economics; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02774557,0.0007105058,0.002940204,0.00165147,0.001068504,0.002258355,0.00277204,0.001976751,0.007153998],"category_scores_gemma":[0.07315803,0.0007931303,0.002641331,0.001851411,0.003277279,0.002956269,0.003367813,0.00407938,0.0005806933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289215,"about_ca_system_score_gemma":0.002400053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004248494,"about_ca_topic_score_gemma":0.003015183,"domain_scores_codex":[0.9804673,0.01451234,0.0006859562,0.00217346,0.001316969,0.0008439207],"domain_scores_gemma":[0.9096304,0.07535094,0.00626956,0.006307107,0.001751689,0.0006902287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003940398,0.0002674927,0.02227079,0.0002817337,0.0008832165,0.0008853302,0.0009315347,0.1509105,0.0007583385,0.7437472,0.002939317,0.07573054],"study_design_scores_gemma":[0.0001807414,0.0001243632,0.006658844,0.0001064658,0.0001789555,0.0001699081,0.0001963394,0.4821986,0.0009814544,0.5025377,0.006593394,0.00007322067],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03039172,0.0002040739,0.966459,0.0006674597,0.00004969782,0.0001450683,0.0002654214,0.000187066,0.00163038],"genre_scores_gemma":[0.7721192,0.0004125646,0.214275,0.0006830117,0.0002819084,0.001360335,0.001082492,0.0001533138,0.009632138],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02774557,"threshold_uncertainty_score":0.1467343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2922863835589675,"score_gpt":0.3783037642443129,"score_spread":0.08601738068534537,"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."}}