{"id":"W1888549093","doi":"10.1515/jci-2015-0021","title":"Design and Analysis of Experiments in Networks: Reducing Bias from Interference","year":2016,"lang":"en","type":"preprint","venue":"Journal of Causal Inference","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Stanford Graduate School of Business; University of California, Davis; York University; Carnegie Mellon University; Johns Hopkins University","keywords":"Estimator; Computer science; Random assignment; Cluster analysis; Graph; Randomized experiment; Interference (communication); Machine learning; Theoretical computer science; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001598336,0.0004771944,0.001930138,0.00123202,0.00002643805,0.00008105155,0.0007719204,0.0004704002,0.0001219381],"category_scores_gemma":[0.002218677,0.0003670914,0.0002541963,0.0005179521,0.0002218751,0.0003746432,0.0007712882,0.001154989,5.993872e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002616179,"about_ca_system_score_gemma":0.0003097686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001207048,"about_ca_topic_score_gemma":0.00002860453,"domain_scores_codex":[0.9959877,0.0005351118,0.00216672,0.000448064,0.0005093008,0.0003531595],"domain_scores_gemma":[0.9914204,0.003844384,0.003254953,0.0006791693,0.0006323443,0.0001687349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.006279028,0.004562884,0.2346206,0.002092857,0.03387955,0.001583831,0.04683184,0.1859835,0.243665,0.07332315,0.003296918,0.1638808],"study_design_scores_gemma":[0.0009917994,0.0007832082,0.008446084,0.01184695,0.002377823,0.00001852511,0.0003210065,0.04171411,0.06157206,0.8708033,0.00001291481,0.00111222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2865548,0.0007580001,0.7120567,0.00003855204,0.0002131061,0.000221848,0.00002435054,0.00002798521,0.0001046785],"genre_scores_gemma":[0.8595647,0.001184821,0.1390685,0.00001961102,0.00008751304,0.00001883802,0.000005472035,0.00003204,0.00001847091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7974802,"threshold_uncertainty_score":0.9998781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2956729877312846,"score_gpt":0.4513108218755544,"score_spread":0.1556378341442698,"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."}}