{"id":"W2033511646","doi":"10.1016/j.csda.2013.09.011","title":"The effect of the prior distribution in the Bayesian Adjustment for Confounding algorithm","year":2013,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Statistics; Confounding; Outcome (game theory); Estimator; Prior probability; Bayesian probability; Mathematics; Econometrics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001182413,0.000131316,0.0002497479,0.0000466508,0.0002770603,0.00008837576,0.0007666058,0.00003438807,0.00002272859],"category_scores_gemma":[0.001790603,0.00006508495,0.00008084647,0.0005351355,0.0001532684,0.0001241774,0.0001741454,0.0001191222,0.000002129624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009001124,"about_ca_system_score_gemma":0.00004056252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001799077,"about_ca_topic_score_gemma":0.0001879043,"domain_scores_codex":[0.9984509,0.0002946535,0.0004520353,0.0002107467,0.0004138675,0.0001778365],"domain_scores_gemma":[0.9893847,0.009387194,0.0003260002,0.0006751132,0.0002036196,0.00002341787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002935372,0.0001287663,0.004183375,0.0001456486,0.001197249,0.00000142932,0.0003341146,0.007786272,0.00001370941,0.6891048,0.03246689,0.2646084],"study_design_scores_gemma":[0.0001692065,0.00005421121,0.01569469,0.00001441168,0.0006300358,8.680198e-7,0.00004758297,0.5806621,0.00003073802,0.4021794,0.0004436146,0.00007311057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001128076,0.00004821485,0.9934658,0.0002603369,0.000042257,0.0009328224,0.004092339,0.00001695904,0.00001321605],"genre_scores_gemma":[0.4598599,0.00003057945,0.532751,0.00006113939,0.00005689166,0.0003516917,0.006837985,0.00001603224,0.00003478332],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5728759,"threshold_uncertainty_score":0.2654088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0632994742926309,"score_gpt":0.4135541767843135,"score_spread":0.3502547024916826,"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."}}