{"id":"W3208912526","doi":"10.1002/cjs.11734","title":"Causal inference for multiple treatments using fractional factorial designs","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Graduate Education; NIH Office of the Director; Faculty of Arts and Sciences","keywords":"Fractional factorial design; Factorial experiment; Causal inference; Factorial; Observational study; Plackett–Burman design; Inference; Design of experiments; Statistics; Computer science; Main effect; Mathematics; Regression; Artificial intelligence; Response surface methodology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002623936,0.0001365686,0.0002459873,0.0002149947,0.0004013379,0.00003827287,0.0001902833,0.00004504261,0.0004302433],"category_scores_gemma":[0.002174371,0.0001424914,0.00005731902,0.000104002,0.00006094229,0.0001528795,0.00001679761,0.0002838569,7.737552e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104275,"about_ca_system_score_gemma":0.00212057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001329632,"about_ca_topic_score_gemma":0.003887336,"domain_scores_codex":[0.998846,0.00007058676,0.000427806,0.000103477,0.0002604014,0.0002917238],"domain_scores_gemma":[0.9972396,0.001493298,0.0004410626,0.0001136659,0.0003814553,0.0003309296],"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":[0.0006511208,0.0005173258,0.03838066,0.0002017681,0.001038406,0.001479223,0.004860755,0.01304142,0.009476266,0.810068,0.1041986,0.01608642],"study_design_scores_gemma":[0.001408499,0.00110936,0.0005552351,0.00003942408,0.0002179281,0.0002473443,0.0004843461,0.005532931,0.0009663316,0.9742233,0.01484769,0.0003676806],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01760117,0.00002236839,0.9782729,0.00002403342,0.0008540782,0.0002344521,0.002938025,0.00001229887,0.00004073786],"genre_scores_gemma":[0.5710195,0.000002484674,0.4286189,0.00003458279,0.0001903613,0.00001514057,0.00002644146,0.00002519056,0.00006739733],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5534183,"threshold_uncertainty_score":0.5810631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3394640169358276,"score_gpt":0.4273499025024903,"score_spread":0.08788588556666277,"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."}}