{"id":"W4386790508","doi":"10.31234/osf.io/k2gzp","title":"The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Institut de Valorisation des Données","keywords":"Standardization; Propensity score matching; Computation; Computer science; Econometrics; Statistics; Mathematics; Algorithm; Operating system","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006837324,0.002314725,0.001350489,0.002751772,0.001018244,0.002225255,0.002650035,0.00248025,0.06143509],"category_scores_gemma":[0.03833308,0.001325924,0.001948451,0.003240109,0.002126898,0.003552426,0.00263166,0.005959441,0.02215861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119267,"about_ca_system_score_gemma":0.002487532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003023364,"about_ca_topic_score_gemma":0.004927902,"domain_scores_codex":[0.997265,0.00143173,0.0002306113,0.0003367167,0.0006519457,0.00008394128],"domain_scores_gemma":[0.9927007,0.005038389,0.0002814407,0.001190808,0.0006370094,0.0001516291],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004008618,0.00006466223,0.0002772401,0.0005033794,0.00006467789,0.00023423,0.0002497829,0.009924321,0.0006425283,0.7384632,0.09060938,0.1589265],"study_design_scores_gemma":[0.00004872679,0.00002253402,0.0002118045,0.0001401843,0.00002248795,0.0001710963,0.00003577103,0.0282926,0.0004926524,0.8611014,0.1094055,0.00005522169],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001520671,0.000592173,0.9932911,0.0007790836,0.0003138197,0.0001002263,0.0005697436,0.00112776,0.003073986],"genre_scores_gemma":[0.004249913,0.0009799259,0.9864568,0.000550047,0.0002973326,0.0006369416,0.0004745665,0.001481653,0.004872854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9931627,"threshold_uncertainty_score":0.2055209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3444528684748194,"score_gpt":0.4287171338974771,"score_spread":0.08426426542265769,"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."}}