{"id":"W4292760984","doi":"10.1111/insr.12518","title":"Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference","year":2022,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Missing data; Causal inference; Calibration; Sampling (signal processing); Empirical likelihood; Computer science; Weighting; Data mining; Statistics; Econometrics; Mathematics; Artificial intelligence; Machine learning","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":[0.07117372,0.001895775,0.002902286,0.007402618,0.001259866,0.00279844,0.003950667,0.003425827,0.004557342],"category_scores_gemma":[0.1447654,0.001191794,0.002854618,0.01073943,0.006823874,0.005941036,0.003434312,0.006502775,0.001219838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003484537,"about_ca_system_score_gemma":0.005732665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003244926,"about_ca_topic_score_gemma":0.001615349,"domain_scores_codex":[0.9559482,0.03306146,0.001870794,0.003109108,0.005525996,0.0004843805],"domain_scores_gemma":[0.8664342,0.1110566,0.004961743,0.008076054,0.00902153,0.0004500186],"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":[0.00004363303,0.00004927145,0.001617512,0.004186347,0.0004838945,0.00009490024,0.0005386927,0.01717605,0.0002368802,0.6952487,0.009581427,0.2707428],"study_design_scores_gemma":[0.00003253426,0.00006862987,0.001984166,0.003358521,0.0001761872,0.0003052915,0.0002457472,0.02396382,0.0008381914,0.8913559,0.07756587,0.0001050839],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001399427,0.0940605,0.891568,0.006667214,0.0007789297,0.000153897,0.0002377818,0.0002212955,0.004912922],"genre_scores_gemma":[0.1241127,0.2564418,0.6030918,0.005367868,0.005236515,0.001469958,0.0007125878,0.0003543879,0.003212283],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07117372,"threshold_uncertainty_score":0.3764071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4095992104995709,"score_gpt":0.5409369624078981,"score_spread":0.1313377519083272,"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."}}