{"id":"W1967188199","doi":"10.2202/1557-4679.1198","title":"Comparing Approaches to Causal Inference for Longitudinal Data: Inverse Probability Weighting versus Propensity Scores","year":2010,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Propensity score matching; Causal inference; Inverse probability weighting; Estimator; Covariate; Statistics; Inverse probability; Weighting; Observational study; Average treatment effect; Econometrics; Marginal structural model; Mathematics; Inference; Mean squared error; Global Positioning System; Confidence interval; Computer science; Bayesian probability; Medicine; Posterior probability; Artificial intelligence","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":[0.1670422,0.001882162,0.003011372,0.00520213,0.001211371,0.003447858,0.003673668,0.003432418,0.003956726],"category_scores_gemma":[0.4543107,0.0009666079,0.003978767,0.006438303,0.005080252,0.007334548,0.006196868,0.00496565,0.0005161311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002187781,"about_ca_system_score_gemma":0.003448799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002322834,"about_ca_topic_score_gemma":0.002036088,"domain_scores_codex":[0.791556,0.1910546,0.003865244,0.004471632,0.008401134,0.0006514592],"domain_scores_gemma":[0.4785621,0.4755459,0.01340466,0.02378196,0.007621741,0.001083744],"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":[0.001113901,0.0003291646,0.0169524,0.002056739,0.004419539,0.0001601343,0.001655332,0.0840795,0.000479427,0.5529864,0.002187887,0.3335796],"study_design_scores_gemma":[0.0007090471,0.0006841912,0.005400006,0.0006302241,0.000780085,0.0001936143,0.0003374199,0.1928092,0.0007579267,0.7909673,0.006600848,0.0001302255],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01377289,0.003558679,0.9790729,0.001537277,0.0001691316,0.0004164955,0.0001182675,0.00008697678,0.001267541],"genre_scores_gemma":[0.2100504,0.005473863,0.7791818,0.001004544,0.000482618,0.002371414,0.0003760782,0.0001368215,0.0009224134],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8329579,"threshold_uncertainty_score":0.883414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6633736756072357,"score_gpt":0.4530269615981859,"score_spread":0.2103467140090498,"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."}}