{"id":"W2952566994","doi":"10.1214/19-ejs1566","title":"Empirical likelihood inference for non-randomized pretest-posttest studies with missing data","year":2019,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Empirical likelihood; Mathematics; Statistics; Confidence interval; Wald test; Missing data; Score test; Inference; Propensity score matching; Randomized experiment; Statistic; Coverage probability; Likelihood-ratio test; Test statistic; Restricted maximum likelihood; Statistical hypothesis testing; Econometrics; Maximum likelihood; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1103455,0.001778665,0.004341978,0.004527866,0.001045285,0.003339529,0.006127092,0.00340177,0.01070102],"category_scores_gemma":[0.4093603,0.001554994,0.003358074,0.004110874,0.003793819,0.005213647,0.003544563,0.004494224,0.001117335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001745699,"about_ca_system_score_gemma":0.003118732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972243,"about_ca_topic_score_gemma":0.00132481,"domain_scores_codex":[0.9388063,0.05169942,0.002249985,0.003283019,0.003410793,0.0005504045],"domain_scores_gemma":[0.5203869,0.4442106,0.01428292,0.01617774,0.004117675,0.0008240892],"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":[0.0009013703,0.0003468679,0.01764976,0.002435794,0.001751863,0.001242851,0.0009201024,0.1129869,0.0008420289,0.6161676,0.005333388,0.2394215],"study_design_scores_gemma":[0.0004048328,0.0002584082,0.00195189,0.0004354795,0.0003518689,0.0003231455,0.0001430446,0.3655489,0.001232974,0.6253164,0.003976828,0.00005613258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002830345,0.0005209382,0.9950758,0.0003612977,0.00004624447,0.0002182846,0.000159586,0.0002135463,0.000574033],"genre_scores_gemma":[0.2254801,0.001465576,0.7650897,0.0006544045,0.000373171,0.003508947,0.0009861364,0.0002584584,0.002183531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1103455,"threshold_uncertainty_score":0.5835695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1420432733550241,"score_gpt":0.4623509266687351,"score_spread":0.320307653313711,"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."}}