Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 240800160-8.doi: 10.11896/jsjkx.240800160
• Artificial Intelligence • Previous Articles Next Articles
ZHOU Chan, WEI Zhengxi, LIU Jiang, CHEN Yuwen
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| [1]CUI L,HOU Y F,ZHANG H.On medical decision support sys-tem[J].Journal of Medical Informatics,2003(1):28-30,40. [2]PEARL J.Causal inference in statistics:An overview[J].Statistics Surveys.2009,3:96-146. [3]PEARL J.Causal inference[C]//Proceedings of the 2008th International Conference on Causality:Objectives and Assessment.JMLR.org,2008. [4]NEUBERG L G.Causality:models,reasoning,and inference,by Judea Pearl,Cambridge University Press,2000[J].Econometric Theory,2003,19(4):675-685. [5]LEWIS D.Causation[J].Journal of Philosophy,1973,70(17):556-567. [6]WOODWARD J.Making Things Happen:A Theory of CausalExplanation[M].Oxford University Press,2004. [7]DANKS D.Singular Causation[M]//The Oxford Handbook of Causal Reasoning,Waldmann M R,Oxford University Press,2017:201-216. [8]GLYMOUR C,DANKS D,GLYMOUR B,et al.Actual causa-tion:a stone soup essay[J].Synthese,2010,175(2):169-192. [9]RICHENS J G,LEE C M,JOHRI S.Improving the accuracy of medical diagnosis with causal machine learning[J].Nature Communications.2020,11:3923. [10]PEARL J.Causal diagrams for empirical research[J].Biometrika,1995,82(4):669-688. [11]REINHOLD J C,CARASS A,PRINCE J L.A Structural Causal Model for MR Images of Multiple Sclerosis[J].arXiv:2103.03158,2021. [12]RUBIN D B.Bayesian Inference for Causal Effects:The Role of Randomization[J].The Annals of Statistics.1978,6(1):34-58,25. [13]NOGUEIRA A R,PUGNANA A,RUGGIERI S,et al.Methods and tools for causal discovery and causal inference[J].WIREs Data Mining and Knowledge Discovery,2022,12(2):e1449. [14]YAO L,CHU Z,LI S,et al.A Survey on Causal Inference[J].ACM Transactions on Knowledge Discovery form Data,2021,15(5):1-46. [15]FEHR J,PICCININNI M,KURTH T,et al.Assessing thetransportability of clinical prediction models for cognitive impairment using causal models[J].BMC Medical Research Me-thodology.2023,23(1):187. [16]PAVLOVIĆ M,AL HAJJ G S,KANDURI C,et al.Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics[J].Nature Machine Intelligence,2024,6(1):15-24. [17]RICHENS J G,LEE C M,JOHRI S.Improving the accuracy of medical diagnosis with causal machine learning[J].Nature Communications,2020,11(1):3923. [18]SCHÖLKOPF B.Causality for Machine Learning[M]//Probabilistic and Causal Inference:The Works of Judea Pearl.Association for Computing Machinery,2022:765-804. [19]KIM B,KHANNA R,KOYEJO O.Examples are not enough,learn to criticize! criticism for interpretability[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.Curran Associates Inc.,2016. [20]DOSHI-VELEZ F,KIM B.Towards A Rigorous Science of Interpretable Machine Learning[J].arXiv:1702.08608,2017. [21]GILPIN L,BAU D,YUAN B Z,et al.Explaining Explanations:An Overview of Interpretability of Machine Learning[C]//2018 IEEE 5th International Conference on Data Science and Advanced Analytics(DSAA).2018. [22]KIM C,BASTANI O.Learning Interpretable Models with Cau-sal Guarantees[J].arXiv:1901.08576,2019. [23]KRZYWINSKI M,ALTMAN N.Classification and regressiontrees[J].Nature Methods,2017,14(8):757-758. [24]TIBSHIRANI R.Regression Shrinkage and Selection Via the Lasso[J].Journal of the Royal Statistical Society:Series B(Methodological),2018,58(1):267-288. [25]USTUN B,RUDIN C.Supersparse linear integer models for optimized medical scoring systems[J].Machine Learning,2016,102(3):349-391. [26]CARUANA R,LOU Y,GEHRKE J,et al.Intelligible Models for HealthCare:Predicting Pneumonia Risk and Hospital 30-day Readmission[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.Association for Computing Machinery,2015. [27]ANGELINO E,LARUS-STONE N,ALABI D,et al.Learning Certifiably Optimal Rule Lists[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.Association for Computing Machinery,2017. [28]ELLIS K,SOLAR-LEZAMA A,TENENBAUM J B.Unsupervised learning by program synthesis[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.MIT,2015. [29]VERMA A,MURALI V,SINGH R,et al.Programmatically Interpretable Reinforcement Learning[J].arXiv:1804.02477,2018. [30]VALKOV L,CHAUDHARI D,SRIVASTAVA A,et al.HOUDINI:Lifelong Learning as Program Synthesis[C]//Neural Information Processing Systems.2018. [31]ELLIS K,RITCHIE D,SOLAR-LEZAMA A,et al.Learning to infer graphics programs from hand-drawn images[C]//Procee-dings of the 32nd International Conference on Neural Information Processing Systems.Curran Associates Inc.,2018. [32]LI Y,MA S,LE T,et al.Causal Decision Trees[J].IEEETransactions on Knowledge and Data Engineering,2017,29(2):257-271. [33]LI J,WU B,SUN X,et al.Causal Hidden Markov Model for Time Series Disease Forecasting[C]//2021 IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition(CVPR).2021:12100-12109. [34]GERSTENBERG T,GOODMAN N D,LAGNADO D A,et al.A counterfactual simulation model of causal judgments for physical events[J].Psychological Review,2021,128(5):935-975. [35]JACKSON C.Random forest classification of multiple sclerosis clinical courses[J].Frontiers in Neurology, 2017,8:1-10. [36]FLAUZINO T,SIMÃO A N C,DE CARVALHO JENNINGS PEREIRA W L,et al.Disability in multiple sclerosis is associa-ted with age and inflammatory,metabolic and oxidative/nitrosative stress biomarkers:results of multivariate and machine lear-ning procedures[J].Metabolic Brain Disease,2019,34:1401-1413. [37] BICA I,ALAA A,LAMBERT C,et al.From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning:Current and Future Methods to Address Underlying Challenges[J].Clinical Pharmacology & Therapeutics,2020,109(1):87-100. [38]WILKINSON J,ARNOLD K F,MURRAY E J,et al.Time to reality check the promises of machine learning-powered precision medicine[J].The Lancet Digital health,2020,2(12):e677-e680. [39]GRUBER S,LAAN M V D.tmle:An R Package for Targeted Maximum Likelihood Estimation[J].Journal of Statistical Software,2012,51(13):1-35. [40]AUSTIN P C,STUART E A.Moving towards best practicewhen using inverse probability of treatment weighting(IPTW) using the propensity score to estimate causal treatment effects in observational studies[J].Statistics in Medicine,2015,34:3661-3679. [41]CHIPMAN H A,GEORGE E I,MCCULLOCH R E.BART:Bayesian Additive Regression Trees[J].The Annals of Applied Statistics,2008,4:266-298. [42]HILL J L.Bayesian Nonparametric Modeling for Causal Infe-rence[J].Journal of Computational and Graphical Statistics,2011,20:217-240. [43]WAGER S,ATHEY S.Estimation and Inference of Heterogeneous Treatment Effects using Random Forests[J].Journal of the American Statistical Association,2015,113:1228-1242. [44]ROSENBAUM P R,RUBIN D B.The central role of the propensity score in observational studies for causal effects[J].Biometrika,1983,70:41-55. [45]DEHEJIA R H,WAHBA S.Propensity Score-Matching Me-thods for Nonexperimental Causal Studies[J].The Review of Economics and Statistics,2002,84(1):151-161. [46]HO D,IMAI K,KING G,et al.MatchIt:Nonparametric Preprocessing for Parametric Causal Inference[J].Journal of Statistical Software,2011,42(8):1-28. [47]OPRESCU M,SYRGKANIS V,BATTOCCHI K,et al.Econ-ML:A Machine Learning Library for Estimating Heterogeneous Treatment Effects[EB/OL].https://mirunaoprescu.com/assets/publications/2019_econml/EconML_Workshop_Poster.pdf. [48]DUDÍK M,LANGFORD J,LI L.Doubly Robust Policy Evaluation and Learning[C]//International Conference on Machine Learning.2011. [49]DUDÍK M,ERHAN D,LANGFORD J,et al.Doubly RobustPolicy Evaluation and Optimization[J].arXiv:1503.02834,2015. [50]TIAN J.Identifying Dynamic Sequential Plans[C]//Conference on Uncertainty in Artificial Intelligence.2008. [51]DUONG T D,LI Q,XU G.Stochastic intervention for causal inference via reinforcement learning[J].Neurocomputing,2022,482:40-49. [52]SHEN X,MA S,VEMURI P,et al.Challenges and Opportunities with Causal Discovery Algorithms:Application to Alzheimer’s Pathophysiology[J].Scientific Reports,2020,10(1):2975. [53]ZHANG F,BARANOVA A,ZHOU C,et al.Causal influences of neuroticism on mental health and cardiovascular disease[J].Human Genetics,2021,140(9):1267-1281. [54]HARTMANN M,FENTON N,DOBSON R.Current reviewand next steps for artificial intelligence in multiple sclerosis risk research[J].Computers in Biology and Medicine,2021,132:104337. [55]TOPUZ K,ZENGUL F D,DAGˇ A,et al.Predicting graft survi-val among kidney transplant recipients:A Bayesian decision support model[J].Decision Support Systems,2018,106:97-109. [56]DAHABREH I J,BIBBINS-DOMINGO K.Causal InferenceAbout the Effects of Interventions From Observational Studies in Medical Journals[J].JAMA,2024,331(21):1845-1853. |
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