{"id":"W4283789559","doi":"10.1002/cjs.11718","title":"Causal inference: Critical developments, past and future","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Causal inference; Causality (physics); Statistical inference; Inference; Field (mathematics); Epistemology; Subject (documents); Causal model; Data science; Computer science; Econometrics; Mathematics; Philosophy; 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":[0.00034773,0.0001243804,0.0002277117,0.0002095618,0.0003099134,0.00005221806,0.0001928457,0.00004472831,0.000779663],"category_scores_gemma":[0.001001971,0.0001253096,0.00001910943,0.0001366843,0.0001539457,0.0001143221,0.00004251976,0.0005284122,0.000001516096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002756501,"about_ca_system_score_gemma":0.001286834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001444396,"about_ca_topic_score_gemma":0.001682675,"domain_scores_codex":[0.9988688,0.00007828015,0.0004014001,0.00009743666,0.0002622824,0.0002917693],"domain_scores_gemma":[0.9983376,0.0005593572,0.000170541,0.0001064335,0.0003085389,0.0005175328],"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":[0.000009016035,0.00001768139,0.003597471,0.00004507163,0.00002812306,0.0008449107,0.001096903,0.000003102917,0.00002541147,0.9337101,0.04471874,0.01590346],"study_design_scores_gemma":[0.0002530267,0.0003519615,0.003506423,0.00003630205,0.00005338503,0.0008484917,0.001916611,0.00003382507,0.00004943603,0.8457481,0.1469243,0.0002781475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03644624,0.001138206,0.9479787,0.004798409,0.002553373,0.0004175121,0.002905006,0.00007863078,0.003683969],"genre_scores_gemma":[0.5431296,0.00005684599,0.4560011,0.0003472445,0.0002970902,0.000008193584,0.00001075986,0.00002979636,0.0001194309],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5066833,"threshold_uncertainty_score":0.8536763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07887559240728977,"score_gpt":0.367192966318878,"score_spread":0.2883173739115882,"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."}}