{"id":"W1998147618","doi":"10.5555/3191835.3191958","title":"Adjustments to propensity score matching for network structures","year":2014,"lang":"en","type":"article","venue":"Advances in Social Networks Analysis and Mining","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Imperial Bank of Commerce (Canada)","funders":"","keywords":"Propensity score matching; Computer science; Inference; Metric (unit); Matching (statistics); Data mining; Observational study; Robustness (evolution); Machine learning; Artificial intelligence; Econometrics; Statistics; Mathematics; Engineering","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":[0.09013803,0.001021531,0.00115076,0.00268739,0.001646673,0.003129045,0.003010109,0.002034711,0.01038587],"category_scores_gemma":[0.3499242,0.0008474458,0.002269621,0.004545471,0.003152951,0.004940479,0.003533731,0.003851198,0.001491358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002323947,"about_ca_system_score_gemma":0.002894176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002785863,"about_ca_topic_score_gemma":0.002126261,"domain_scores_codex":[0.9021446,0.07814462,0.003727402,0.008019296,0.006826409,0.001137701],"domain_scores_gemma":[0.7913286,0.1279543,0.01554115,0.05703098,0.007200038,0.0009448326],"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":[0.001095612,0.0003635467,0.04070523,0.0006635653,0.001542251,0.0002806464,0.001912612,0.04160795,0.003618601,0.4719758,0.01372557,0.4225086],"study_design_scores_gemma":[0.0005105716,0.0007141769,0.0279687,0.0002537003,0.0006249422,0.0003626841,0.0003626684,0.1196315,0.00615854,0.79285,0.05041876,0.0001437027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009268376,0.0001931402,0.9848585,0.001139374,0.0001968848,0.0008167803,0.0003828685,0.0004719522,0.002672156],"genre_scores_gemma":[0.3096652,0.0003303924,0.6797278,0.001051075,0.0002684161,0.00389692,0.0008348562,0.000360158,0.003865114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09013803,"threshold_uncertainty_score":0.4767012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08983792281091064,"score_gpt":0.394851842228658,"score_spread":0.3050139194177474,"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."}}