{"id":"W3021067680","doi":"10.1136/injuryprev-2020-savir.37","title":"107 Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions?","year":2020,"lang":"en","type":"article","venue":"Oral Presentations","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Causal inference; Psychological intervention; Interrupted time series; Observational study; Computer science; Inference; Interrupted Time Series Analysis; Confounding; Risk analysis (engineering); Control (management); Time series; Poison control; Rigour; Management science; Engineering; Machine learning; Econometrics; Artificial intelligence; Psychology; Medicine; Environmental health; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"category_scores_codex":[0.4481561,0.002082433,0.003144145,0.002593095,0.001975901,0.00632195,0.004177532,0.005077139,0.0174588],"category_scores_gemma":[0.7487587,0.001550829,0.006916263,0.003507611,0.007780702,0.006961392,0.004904823,0.005076493,0.001665569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004264525,"about_ca_system_score_gemma":0.007419757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003402131,"about_ca_topic_score_gemma":0.002680363,"domain_scores_codex":[0.4005925,0.5631166,0.01483174,0.009871237,0.01043911,0.001148836],"domain_scores_gemma":[0.1908282,0.7091827,0.03483493,0.0521379,0.01203916,0.0009770395],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009525068,0.0009001922,0.0360866,0.01763901,0.01003903,0.0005290952,0.006542748,0.05087278,0.001058996,0.5222601,0.02217672,0.3223696],"study_design_scores_gemma":[0.006718711,0.007216698,0.02154061,0.01360523,0.005209832,0.0003907478,0.001667174,0.1780352,0.003523732,0.6902325,0.07136916,0.0004905375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02741514,0.006156237,0.9228002,0.01566349,0.003585457,0.009996797,0.002106948,0.0007806658,0.01149509],"genre_scores_gemma":[0.4541592,0.001867395,0.5014614,0.007115637,0.00136677,0.03089093,0.001306657,0.0002227516,0.001609149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5518439,"threshold_uncertainty_score":0.6805218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3480507879678849,"score_gpt":0.5051622501842452,"score_spread":0.1571114622163603,"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."}}