{"id":"W1981158530","doi":"10.1002/cjs.11162","title":"Q‐learning for estimating optimal dynamic treatment rules from observational data","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research; Mailman School of Public Health, Columbia University; National Institutes of Health","keywords":"Observational study; Covariate; Randomized experiment; Propensity score matching; Causal inference; Randomized controlled trial; Computer science; Analysis of covariance; Machine learning; Inference; Econometrics; Vocabulary; Statistics; Set (abstract data type); Artificial intelligence; Medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0003502975,0.0001390508,0.0002546372,0.00009731432,0.0001579825,0.00005092651,0.0002844944,0.00005748753,0.0001141517],"category_scores_gemma":[0.003098198,0.0001281263,0.00003226685,0.0000443607,0.00005921393,0.0003967207,0.00002011343,0.0001630766,0.000003946758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004049471,"about_ca_system_score_gemma":0.0006872784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006979037,"about_ca_topic_score_gemma":0.002712811,"domain_scores_codex":[0.9989473,0.00003799621,0.0004459952,0.00009976014,0.0001388876,0.0003300212],"domain_scores_gemma":[0.9971786,0.001513743,0.0004346197,0.0002206976,0.0002559044,0.0003964262],"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":[0.0001144117,0.0003173509,0.04155406,0.000286085,0.001025501,0.0002396723,0.008383714,0.006907481,0.0008335639,0.4556656,0.05155256,0.43312],"study_design_scores_gemma":[0.001096844,0.0008511296,0.007245143,0.0003595706,0.0005252986,0.0001286569,0.001005674,0.3361853,0.0002251953,0.6318231,0.01992542,0.0006286107],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03420352,0.000163111,0.961759,0.00008397058,0.0002641325,0.000133586,0.00333325,0.000016848,0.00004258737],"genre_scores_gemma":[0.1119761,0.00001124853,0.8870721,0.00002640157,0.0002588198,0.00000544862,0.0005491818,0.00002940848,0.00007122002],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4324914,"threshold_uncertainty_score":0.5224836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3868564774755924,"score_gpt":0.4250497342740967,"score_spread":0.03819325679850433,"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."}}