{"id":"W4293064632","doi":"10.1002/cjs.11702","title":"Dynamic treatment regimes with interference","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ontario Institute for Cancer Research","keywords":"Robustness (evolution); Observational study; Computer science; Ordinary least squares; Econometrics; Function (biology); Population; Data mining; Machine learning; Medicine; Mathematics; Statistics","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":[0.0001406697,0.0001192076,0.0002197749,0.0001962973,0.0001618656,0.00002710366,0.0002176478,0.00001798791,0.0005142036],"category_scores_gemma":[0.0001616508,0.00009665784,0.0000267186,0.0001220233,0.00009565682,0.00007101946,0.00001334488,0.0002279632,0.000001818033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009291787,"about_ca_system_score_gemma":0.00129405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006758691,"about_ca_topic_score_gemma":0.01556621,"domain_scores_codex":[0.9992031,0.00005527912,0.0002791973,0.00008372401,0.0001604222,0.0002182901],"domain_scores_gemma":[0.9988474,0.0002291705,0.0003002925,0.0001674292,0.0001748517,0.0002808962],"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":[0.0001746653,0.0001702104,0.002720042,0.00007561962,0.0003461586,0.00844186,0.006914166,0.0006033843,0.0001909264,0.8770046,0.04728623,0.05607215],"study_design_scores_gemma":[0.0008737725,0.006474811,0.0004040474,0.000132723,0.000187918,0.002919962,0.003736818,0.0007433399,0.0003999927,0.9555932,0.02807177,0.0004616856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04177201,0.0002308294,0.9539613,0.0004366336,0.0002512136,0.0002360479,0.0009367876,0.00003446256,0.002140768],"genre_scores_gemma":[0.7125004,0.00002033293,0.286415,0.00005246123,0.00001335081,0.00001002918,0.000006889218,0.00002339226,0.0009581267],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6707284,"threshold_uncertainty_score":0.8686312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07877763027666349,"score_gpt":0.3395120526457993,"score_spread":0.2607344223691358,"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."}}