{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04708746,0.000876094,0.001384671,0.004546159,0.001665811,0.006857998,0.001994708,0.004009293,0.006202821],"category_scores_gemma":[0.09092644,0.0005388213,0.0006436347,0.003301098,0.01733557,0.01211727,0.002595808,0.009918913,0.0008703226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006301596,"about_ca_system_score_gemma":0.005176709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00360893,"about_ca_topic_score_gemma":0.002041047,"domain_scores_codex":[0.988784,0.007409788,0.0004397364,0.00119104,0.001889084,0.0002863117],"domain_scores_gemma":[0.7910047,0.1879655,0.003494072,0.004018342,0.01176009,0.00175729],"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":[0.0000489889,0.00002552286,0.0004319541,0.0008343079,0.00004265743,0.0001078816,0.0002942905,0.001170957,0.0000692691,0.9348651,0.01523352,0.0468756],"study_design_scores_gemma":[0.00001305834,0.00001501153,0.0003012752,0.001203911,0.00001774185,0.0001474907,0.0002566718,0.002356733,0.0001127672,0.9415275,0.05402348,0.00002438974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002555994,0.6627829,0.05320761,0.2583729,0.005721789,0.00002939017,0.0001586568,0.00008379902,0.0170869],"genre_scores_gemma":[0.2479343,0.633996,0.04030441,0.02018453,0.05323525,0.0001345585,0.0001798081,0.0001431192,0.003888226],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.04708746,"threshold_uncertainty_score":0.2490253,"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."}}