{"id":"W3213843957","doi":"10.3390/e24020179","title":"Normalized Augmented Inverse Probability Weighting with Neural Network Predictions","year":2022,"lang":"en","type":"article","venue":"Entropy","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Estimator; Weighting; Regularization (linguistics); Computer science; Artificial neural network; Stochastic gradient descent; Parametric statistics; Mathematical optimization; Mathematics; Normalization (sociology); Robustness (evolution); Algorithm; Artificial intelligence; Statistics","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":[0.008270949,0.000987052,0.00165841,0.0009462725,0.000393711,0.001320192,0.002499182,0.001612181,0.003218914],"category_scores_gemma":[0.03414416,0.0006438332,0.0008439968,0.001062877,0.001409932,0.003246324,0.001724386,0.002499789,0.0004794929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136192,"about_ca_system_score_gemma":0.001818212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004566376,"about_ca_topic_score_gemma":0.003395599,"domain_scores_codex":[0.9969421,0.001718055,0.000154001,0.0005773014,0.0004710961,0.0001374273],"domain_scores_gemma":[0.9885021,0.008134372,0.0008842872,0.001383797,0.0009398549,0.0001555645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002818706,0.0001085247,0.002119638,0.0001439005,0.0001509898,0.0001040926,0.0001231931,0.7893826,0.001176946,0.08069726,0.001344322,0.1243666],"study_design_scores_gemma":[0.00002046061,0.00002165937,0.0002733673,0.00001283113,0.00001348723,0.00001572849,0.000003725728,0.9648247,0.0004844613,0.03397677,0.0003417861,0.00001097018],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01874503,0.0001870624,0.9791835,0.0003216932,0.00005805,0.00006582985,0.0001134871,0.0002886682,0.00103678],"genre_scores_gemma":[0.5747414,0.0003899912,0.4181742,0.0004164061,0.000190402,0.0004821594,0.0005652774,0.0001186601,0.004921387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008270949,"threshold_uncertainty_score":0.04374146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0720494997390497,"score_gpt":0.326328666726839,"score_spread":0.2542791669877893,"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."}}