因果学习方法和应用概述

doi:10.3778/j.issn.1002-8331.2405-0407

摘要/Abstract

摘要： 机器学习是人工智能和数据科学的核心所在，被广泛应用于教育、交通运输和制造业等领域；随着机器学习的发展及应用领域的延伸，模型在可解释性和公平性等方面显现了一些需要解决的问题。因果学习作为一种将因果关系和机器学习技术相结合的方法，可以增强模型的可解释性，解决公平性等问题，其研究已逐渐成为学术界的热点。因此，在介绍因果学习的相关理论知识的基础上，根据因果学习所能解决的问题对因果解释、因果监督学习、因果公平、因果强化学习等技术进行了全方位的分析概述；从多角度归纳了因果学习在医学、农业和智能制造等领域的应用。最后，总结了因果学习存在的一些开放性问题和挑战，并给出了未来的研究方向，旨在推动因果学习的不断发展。

关键词: 机器学习, 因果关系, 因果学理论, 因果模型, 因果学习技术, 因果学习应用

Abstract: Machine learning is the core of artificial intelligence and data science, and is widely used in education, transportation and manufacturing. With the development of machine learning and the extension of application fields, the models have revealed some problems to be solved in terms of interpretability and fairness. Causal learning (CL), as a method combining causality and machine learning techniques, can enhance the interpretability of the model and solve the problems of fairness, and its research has gradually become a hot spot in the academic world. Therefore, based on the introduction of the relevant theoretical knowledge of CL, the techniques of causal explanation, causal supervised learning, causal fairness, and causal reinforcement learning are firstly analyzed and outlined in an all-round way according to the problems that can be solved by CL. Secondly, the applications of CL in the fields of medicine, agriculture and intelligent manufacturing are summarized from multiple perspectives. Finally, some open problems and challenges of CL are summarized, and future research directions are given, aiming to promote the continuous development of CL.

Key words: machine learning, causal relationship, causal theory, causal model, causal learning (CL) techniques, application of causal learning (CL)

龙享福, 李少波, 张仪宗, 杨磊, 李传江. 因果学习方法和应用概述[J]. 计算机工程与应用, 2024, 60(24): 1-19.

LONG Xiangfu, LI Shaobo, ZHANG Yizong, YANG Lei, LI Chuanjiang. Overview of Causal Learning Techniques and Applications[J]. Computer Engineering and Applications, 2024, 60(24): 1-19.

参考文献

[1] ARTI S, HIDAYAH I, KUSUMAWARDANI S S. Research trend of causal machine learning method: a literature review[J]. International Journal on Informatics for Development, 2020, 9(2): 111-118.
[2] WUEST T, WEIMER D, IRGENS C, et al. Machine learning in manufacturing: advantages, challenges, and applications[J]. Production & Manufacturing Research, 2016, 4(1): 23-45.
[3] GUNNING D, AHA D W. Darpa’s explainable artificial inte-
lligence program[J]. AI Magazine, 2019, 40(2): 44-58.
[4] LIPTON Z C. The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery[J]. Queue, 2018, 16(3): 31-57.
[5] D’AMOUR A, HELLER K, MOLDOVAN D, et al. Under specification presents challenges for credibility in modern machine learning[J]. The Journal of Machine Learning Research, 2022, 23(1): 10237-10297.
[6] MEHRABI N, MORSTATTER F, SAXENA N, et al. A survey on bias and fairness in machine learning[J]. ACM Computing Surveys (CSUR), 2021, 54(6): 1-35.
[7] BAROCAS S, HARDT M, NARAYANAN A. Fairness in machine learning[J]. Nips Tutorial, 2017(1): 11-29.
[8] DULAC-ARNOLD G, MANKOWITZ D J, HESTER T. Challenges of real-world reinforcement learning[J]. arXiv:1904.
12901, 2019.
[9] PEARL J. The seven tools of causal inference, with reflections on machine learning[J]. Communications of the ACM, 2019, 62(3): 54-60.
[10] AHMED O, TR?UBLE F, GOYAL A, et al. Causalworld: a robotic manipulation benchmark for causal structure and transfer learning[J]. arXiv:2010.04296, 2020.
[11] SCH?LKOPF B, LOCATELLO F, BAUER S, et al. Toward causal representation learning[J]. Proceedings of the IEEE, 2021, 109(5): 612-634.
[12] GOYAL A, BENGIO Y. Inductive biases for deep learning of higher-level cognition[J]. Proceedings of the Royal Society A, 2022, 478: 20210068-20210103.
[13] MORAFFAH R, KARAMI M, GUO R, et al. Causal interpretability for machine learning-problems, methods and evaluation[J]. ACM SIGKDD Explorations Newsletter, 2020, 22(1): 18-33.
[14] LU C. Learning causal representations for generalization and adaptation in supervised, imitation, and reinforcement learning[D]. Cambridge: University of Cambridge, 2022.
[15] LOFTUS J R, RUSSELL C, KUSNER M J, et al. Causal reasoning for algorithmic fairness[J]. arXiv:1805.05859, 2018.
[16] 李家宁, 熊睿彬, 兰艳艳, 等. 因果机器学习的前沿进展综述[J]. 计算机研究与发展, 2023, 60(1): 59-84.
LI J N, XIONG R B, LAN Y Y, et al. Overview of the frontier progress of causal machine learning[J]. Journal of Computer Research and Development, 2023, 60(1): 59-84.
[17] MAKHLOUF K, ZHIOUA S, PALAMIDESSI C. Survey on causal-based machine learning fairness notions[J]. arXiv:2010.09553, 2020.
[18] YAO L Y, CHU Z X, LI S, et al. A survey on causal inference[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2021, 15(5): 1-46.
[19] 孙悦雯, 柳文章, 孙长银. 基于因果建模的强化学习控制: 现状及展望[J]. 自动化学报, 2023, 49(3): 661-677.
SUN Y W, LIU W Z, SUN C Y. Causality in reinforcement learning control: the state of the art and prospects[J]. Acta Automatica Sinica, 2023, 49(3): 661-677.
[20] ZHANG K X, SUN Q Y, ZHAO C Q, et al. Causal reasoning in typical computer vision tasks[J]. Science China Technological Sciences, 2023, 67(1): 105-120.
[21] VUKOVI? M, THALMANN S. Causal discovery in manufacturing: a structured literature review[J]. Journal of Manufacturing and Materials Processing, 2022, 6(1): 10-27.
[22] PEARL J, MACKENZIE D. The book of why: the new science of cause and effect[M]. New York: Basic Books, 2018.
[23] IMBENS G W, RUBIN D B. Rubin causal model[M]//Microeconometrics. London: Springer, 2010: 229-241.
[24] ZIGLER C M, WATTS K, YEH R W, et al. Model feedback in Bayesian propensity score estimation[J]. Biometrics, 2013, 69(1): 263-273.
[25] ROBINS J M, ROTNITZKY A, ZHAO L P. Estimation of regression coefficients when some regressors are not always observed[J]. Journal of the American Statistical Association, 1994, 89: 846-866.
[26] DUDíK M, LANGFORD J, LI L H. Doubly robust policy evaluation and learning[J]. arXiv:1103.4601, 2011.
[27] RUBIN D B. Randomization analysis of experimental data: the fisher randomization test comment[J]. Journal of the American Statistical Association, 1980, 75: 591-593.
[28] HIRANO K, IMBENS G W, RIDDER G. Efficient estimation of average treatment effects using the estimated propensity score[J]. Econometrica, 2003, 71(4): 1161-1189.
[29] PEARL J. Causality[M]. Cambridge, ?UK: Cambridge University Press, 2009.
[30] VANDERWEELE T. Explanation in causal inference: methods for mediation and interaction[M]. Oxford, UK: Oxford University Press, 2015.
[31] 陈珂锐, 孟小峰. 机器学习的可解释性[J]. 计算机研究与发展, 2020, 57(9): 1971-1986.
CHEN K R, MENG X F. Interpretation and understanding in machine learning[J]. Computer Research and Development, 2020, 57(9): 1971-1986.
[32] LEE S, HOOVER B, STROBELT H, et al. Diffusion explainer: visual explanation for text-to-image stable diffusion[J]. arXiv:2305.03509, 2023.
[33] JIN W, LI X, FATEHI M, et al. Generating post-hoc explanation from deep neural networks for multi-modal medical image analysis tasks[J]. Methods X, 2023, 10: 102009.
[34] CHEN L, CAI X, XING J, et al. Towards transparent deep learning for surface water detection from SAR imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 118: 103287.
[35] CHATTOPADHYAY A, MANUPRIYA P, SARKAR A, et al. Neural network attributions: a causal perspective[C]//Proceedings of the 36th International Conference on Machine Learning, 2019: 981-990.
[36] FRYE C, ROWAT C, FEIGE I. Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability[C]//Advances in Neural Information Processing Systems, 2020: 1229-1239.
[37] HESKES T, SIJBEN E, BUCUR I G, et al. Causal shapley values: Exploiting causal knowledge to explain individual predictions of complex models[C]//Advances in Neural Information Processing Systems, 2020: 4778-4789.
[38] HASAN A B, TOLGA ?. DreaMR: diffusion-driven counterfactual explanation for functional MRI[J]. arXiv:2307.
09547, 2023.
[39] JEANNERET G, SIMON L, JURIE F. Adversarial counterfactual visual explanation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 16425-16435.
[40] 黄珊珊, 王元浩, 龚志黎, 等. 基于因果表征学习的可控图像生成[J]. 信息与电子工程前沿, 2024, 25(1): 135-149.
HUANG S S, WANG Y H, GONG Z L, et al. Controllable image generation based on causal representation learning[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(1): 135-148.
[41] DELANEY E, PAKRASHI A, GREENE D, et al. Counterfactual explanations for misclassified images: How human and machine explanations differ[J]. Artificial Intelligence, 2023, 324: 103995.
[42] ROTEM O, ZARITSKY A. DISentangled counterfactual visual interpretER (DISCOVER) generalizes to natural images[J]. arXiv:2406.15918, 2024
[43] WANG P, VASCONCELOS N. Scout: self-aware discriminant counterfactual explanations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 8981-8990.
[44] KENNY E M, KEANE M T. On generating plausible counterfactual and semi-factual explanations for deep learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 11575-11585.
[45] ABRATE C, BONCHI F. Counterfactual graphs for explainable classification of brain networks[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021: 2495-2504.
[46] GEVAERT A, ROUSSEAU A J, BECKER T, et al. Evaluating feature attribution methods in the image domain[J]. Machine Learning, 2024, 24: 1-46.
[47] BILODEAU B, JAQUES N, KOH P W, et al. Impossibility theorems for feature attribution[J]. Proceedings of the National Academy of Sciences, 2024, 121(2): e2304406120.
[48] KANEHIRA A, TAKEMOTO K, INAYOSHI S, et al. Multimodal explanations by predicting counterfactuality in videos[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 8594-8602.
[49] LI Y C, WANG X, XIAO J B, et al. Invariant grounding for video question answering[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 2928-2937.
[50] ZANG C Q, WANG H Q, PEI M T, et al. Discovering the real association: multimodal causal reasoning in video question answering[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 19027-19036.
[51] TSIRTSIS S, GOMEZ-RODRIGUEZ M. Decisions, counterfactual explanations and strategic behavior[C]//Advances in Neural Information Processing Systems, 2020, 33: 16749-16760.
[52] YANG F, ALVA S S, CHEN J H, et al. Model based counterfactual synthesizer for interpretation[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021: 1964-1974.
[53] HASTIE T, TIBSHIRANI R, FRIEDMAN J, et al. Overview of supervised learning[J]. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009: 9-41.
[54] KADDOUR J, LYNCH A, LIU Q, et al. Causal machine learning: a survey and open problems[J]. arXiv:2206.15475, 2022.
[55] RAHUL M R, CHIDDARWAR S S. A causality-inspired data augmentation approach to cross-domain burr detection using randomly weighted shallow networks[J]. International Journal of Machine Learning and Cybernetics, 2023, 14(12): 4223-4236.
[56] KAUSHIK D, SETLUR A, HOVY E, et al. Explaining the efficacy of counterfactually augmented data[J]. arXiv:2010.
02114, 2020.
[57] TENEY D, ABBASNEDJAD E, VAN DEN HENGEL A. Learning what makes a difference from counterfactual examples and gradient supervision[C]//Proceedings of IEEE/CVF International Conference on Computer Vision (ECCV 2020), 2020: 580-599.
[58] URPí N A, BAGATELLA M, VLASTELICA M, et al. Causal action influence aware counterfactual data augmentation[J]. arXiv:2405.18917v1, 2024.
[59] ARJOVSKY M, BOTTOU L, GULRAJANI I, et al. Invariant risk minimization[J]. arXiv:1907.02893, 2019.
[60] XU J, JI C, CAO Y, et al. Causality-inspired latent feature augmentation for single domain generalization[J]. arXiv:2406.05980, 2024.
[61] WANG Y, YU K, XIANG G, et al. Discovering causally invariant features for out-of-distribution generalization[J]. Pattern Recognition, 2024, 150: 110338.
[62] KRUEGER D, CABALLERO E, JACOBSEN J H, et al. Out-of-distribution generalization via risk extrapolation (REX)[C]//Proceedings of the 38th International Conference on Machine Learning, 2021: 5815-5826.
[63] VEITCH V, D’AMOUR A, YADLOWSKY S, et al. Counterfactual invariance to spurious correlations in text classification[C]//Advances in Neural Information Processing Systems, 2021: 16196-16208.
[64] AHUJA K, CABALLERO E, ZHANG D, et al. Invariance principle meets information bottleneck for out-of-distribution generalization[C]//Advances in Neural Information Processing Systems, 2021: 3438-3450.
[65] PETERS J, BüHLMANN P, MEINSHAUSEN N. Causal inference by using invariant prediction: identification and confidence intervals[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2016, 78(5): 947-1012.
[66] PARASCANDOLO G, KILBERTUS N, ROJAS-CARULLA M, et al. Learning independent causal mechanisms[C]//Proceedings of the 35th International Conference on Machine LearningR, 2018: 4036-4044.
[67] GOYAL A, LAMB A, HOFFMANN J, et al. Recurrent independent mechanisms[J]. arXiv:1909.10893, 2019.
[68] MADAN K, KE N R, GOYAL A, et al. Fast and slow learning of recurrent independent mechanisms[J]. arXiv:2105.08710, 2021.
[69] YUE Z Q, SUN Q R, HUA X S, et al. Transporting causal mechanisms for unsupervised domain adaptation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 8599-8608.
[70] TESHIMA T, SATO I, SUGIYAMA M. Few-shot domain adaptation by causal mechanism transfer[C]//Proceedings of the 37th International Conference on Machine Learning, 2020: 9458-9469.
[71] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[72] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems, 2017: 5998-6008.
[73] KUSNER M J, LOFTUS J, RUSSELL C, et al. Counterfactual fairness[C]//Advances in Neural Information Processing Systems, 2017: 4066-4076.
[74] CHIAPPA S. Path-specific counterfactual fairness[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019: 7801-7808.
[75] NABI R, SHPITSER I. Fair inference on outcomes[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2018: 1931-1940.
[76] ZHU Y C, MA J, WU L, et al. Path-specific counterfactual fairness for recommender systems[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023: 3638-3649.
[77] MA J, GUO R, ZHANG A, et al. Learning for counterfactual fairness from observational data[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023: 1620-1630.
[78] CHEN H, LU W B, SONG R, et al. On learning and testing of counterfactual fairness through data preprocessing[J]. arXiv:2202.12440, 2022.
[79] HAN X, ZHANG L, WU Y K, et al. Achieving counterfactual fairness for anomaly detection[C]//Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2023: 55-66.
[80] HU Y Y, WU Y K, ZHANG L, et al. Fair multiple decision making through soft interventions[C]//Advances in Neural Information Processing Systems, 2020: 17965-17975.
[81] GOEL N, AMAYUELAS A, DESHPANDE A, et al. The importance of modeling data missingness in algorithmic fairness: a causal perspective[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 7564-7573.
[82] PLECKO D, BAREINBOIM E. Causal fairness for outcome control[C]//Advances in Neural Information Processing Systems, 2024: 47575-47597.
[83] ZUO A Q, WEI S S, LIU T L, et al. Counterfactual fairness with partially known causal graph[C]//Advances in Neural Information Processing Systems, 2022: 1238-1252.
[84] ZUO A, LI Y, WEI S, et al. Interventional fairness on partially known causal graphs: a constrained optimization approach[J]. arXiv:2401.10632, 2024.
[85] SUTTON R S, BARTO A G. Reinforcement learning: an introduction[M]. Cambridge: MIT Press, 1998.
[86] BANNON J, WINDSOR B, SONG W, et al. Causality and batch reinforcement learning: complementary approaches to planning in unknown domains[J]. arXiv:2006.02579, 2020.
[87] WEICHWALD S, MOGENSEN S W, LEE T E, et al. Learning by doing: controlling a dynamical system using causality, control, and reinforcement learning[C]//Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2022: 246-258.
[88] ZENG Y, CAI R C, SUN F C, et al. A survey on causal reinforcement learning[J]. arXiv:2302.05209, 2023.
[89] SAUTER A W, ACAR E, FRANCOIS-LAVET V. A meta-reinforcement learning algorithm for causal discovery[C]//Proceedings of Conference on Causal Learning and Reasoning, 2023: 602-619.
[90] LI J, LUO Y, ZHANG X W. Causal reinforcement learning: an instrumental variable approach[J]. arXiv:2103.04021, 2021.
[91] LIAO L F, FU Z Y, YANG Z R, et al. Instrumental variable value iteration for causal offline reinforcement learning[J]. arXiv:2102.09907, 2021.
[92] CHEN S X, ZHANG B. Estimating and improving dynamic treatment regimes with a time-varying instrumental variable[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2023, 85(2): 427-453.
[93] CHEN Y T, XU L Y, GULCEHRE C, et al. On instrumental variable regression for deep offline policy evaluation[J]. The Journal of Machine Learning Research, 2022, 23(1): 13635-13674.
[94] TENNENHOLTZ G, HALLAK A, DALAL G, et al. On covariate shift of latent confounders in imitation and reinforcement learning[J]. arXiv:2110.06539, 2021.
[95] SWAMY G, CHOUDHURY S, BAGNELL D, et al. Causal imitation learning under temporally correlated noise[C]//Proceedings of the 39th International Conference on Machine Learning, 2022: 20877-20890.
[96] BENNETT A, KALLUS N. Proximal reinforcement learning: efficient off-policy evaluation in partially observed markov decision processes[J]. Operations Research, 2023, 3: 1071-1086.
[97] SHI C C, UEHARA M, HUANG J, et al. A minimax learning approach to off-policy evaluation in confounded partially observable Markov decision processes[C]//Proceedings of the 39th International Conference on Machine Learning, 2022: 20057-20094.
[98] GUO J X, GONG M M, TAO D C. A relational intervention approach for unsupervised dynamics generalization in model-based reinforcement learning[J]. arXiv:2206.04551, 2022.
[99] SEITZER M, SCH?LKOPF B, MARTIUS G. Causal influence detection for improving efficiency in reinforcement learning[C]//Advances in Neural Information Processing Systems, 2021: 22905-22918.
[100] SONAR A, PACELLI V, MAJUMDAR A. Invariant policy optimization: towards stronger generalization in reinforcement learning[C]//Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021: 21-33.
[101] ZHU Z M, CHEN X H, TIAN H L, et al. Offline reinforcement learning with causal structured world models[J]. arXiv:2206.01474, 2022.
[102] TENNENHOLTZ G, SHALIT U, MANNOR S, et al. Bandits with partially observable confounded data[C]//Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021: 430-439.
[103] HERLAU T, LARSEN R. Reinforcement learning of causal variables using mediation analysis[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022: 6910-6917.
[104] HU X, ZHANG R, TANG K, et al. Causality-driven hierarchical structure discovery for reinforcement learning[C]//Advances in Neural Information Processing Systems, 2022: 20064-20076.
[105] FENG F, HUANG B W, ZHANG K, et al. Factored adaptation for non-stationary reinforcement learning[C]//Advances in Neural Information Processing Systems, 2022: 31957-31971.
[106] HUANG B W, LU C C, LEQI L, et al. Action-sufficient state representation learning for control with structural constraints[C]//Proceedings of the 39th International Conference on Machine Learning, 2022: 9260-9279.
[107] HUANG B W, FENG F, LU C C, et al. AdaRL: what, where, and how to adapt in transfer reinforcement learning[J]. arXiv:2107.02729, 2021.
[108] BICA I, JARRETT D, VAN DER SCHAAR M. Invariant causal imitation learning for generalizable policies[C]//Advances in Neural Information Processing Systems, 2021: 3952-3964.
[109] WEN C, QIAN J, LIN J, et al. Fighting fire with fire: avoiding dnn shortcuts through priming[C]//Proceedings of the 39th International Conference on Machine Learning, 2022: 23723-23750.
[110] RUAN K, ZHANG J, DI X, et al. Causal imitation learning via inverse reinforcement learning[C]//Proceedings of the 11th International Conference on Learning Representations, Kigali, Rwanda, 1-5 May, 2023.
[111] WILLIAMSON J. Establishing causal claims in medicine[J]. International Studies in Philosophy of Science, 2019, 32(1): 33-61.
[112] RAITA Y, CAMARGO JR C A, LIANG L, et al. Leveraging “big data” in respiratory medicine-data science, causal inference, and precision medicine[J]. Expert Review of Respiratory Medicine, 2021, 15(6): 717-721.
[113] RICHENS J G, LEE C M, JOHRI S. Improving the accuracy of medical diagnosis with causal machine learning[J]. Nature Communications, 2020, 11(1): 3923-3932.
[114] SANCHEZ P, VOISEY J P, XIA T, et al. Causal machine learning for healthcare and precision medicine[J]. Royal Society Open Science, 2022, 9(8): 220638-220653.
[115] HU H, KERSCHBERG L. Improved causal models of Alzheimer’s disease[C]//Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference, 2021: 274-283.
[116] AKNIN E, LAREY A, CALDWELL J M, et al. Harnessing digital pathology and causal learning to improve eosinophilic esophagitis dietary treatment assignment[C]//Proceedings of the 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2023: 1-8.
[117] HU L Y, LIN J Y, SIGEL K, et al. Estimating heterogeneous survival treatment effects of lung cancer screening approaches: a causal machine learning analysis[J]. Annals of Epidemiology, 2021, 62: 36-42.
[118] FEHR J, PICCININNI M, KURTH T, et al. Assessing the transportability of clinical prediction models for cognitive impairment using causal models[J]. BMC Medical Research Methodology, 2023, 23(1): 187-201.
[119] RUST J, AUTEXIER S. Causal inference for personalized treatment effect estimation for given machine learning models[C]//Proceedings of the 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022: 1289-1295.
[120] SHEN X P, MA S S, VEMURI P, et al. A novel method for causal structure discovery from EHR data and its application to type-2 diabetes mellitus[J]. Scientific Reports, 2021, 11(1): 21025-21034.
[121] CASTRO D C, WALKER I, GLOCKER B. Causality matters in medical imaging[J]. Nature Communications, 2020, 11(1): 3673-3683.
[122] YANG C H H, HUNG I T, LIU Y C, et al. Treatment learning causal transformer for noisy image classification[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023: 6139-6150.
[123] DAI Q F, WONG Y K, SUN G F, et al. Unsupervised domain adaptation by causal Learning for biometric signal based HCI[J]. ACM Transactions on Multimedia Computing, Communications and Applications, 2023, 20(2): 1-8.
[124] LI H M, YU S J, PRINCIPE J. Causal recurrent variational autoencoder for medical time series generation[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2023: 8562-8570.
[125] 车翔玖, 武宇宁, 刘全乐. 基于因果特征学习的有权同构图分类算法[J]. 吉林大学学报 (工学版), 2023: 1-6.
CHE X J, WU Y N, LIU Q L. A weighted isomorphic graph classification algorithm based on causal feature learning[J]. Journal of Jilin University (Engineering and Technology Edition), 2023: 1-6.
[126] WU X, LI J W, QIAN Q, et al. Methods and applications of causal reasoning in medical field[C]//Proceedings of the 2021 7th International Conference on Big Data and Information Analytics (BigDIA), 2021: 79-86.
[127] TSOUMAS I, GIANNARAKIS G, SITOKONSTANTINOU V, et al. Evaluating digital agriculture recommendations with causal inference[C]//Proceedings of the AAAI Conference on Artificial IntelligenceI, 2023: 14514-14522.
[128] GIANNARAKIS G, SITOKONSTANTINOU V, LORILLA R S, et al. Personalizing sustainable agriculture with causal machine learning[R]. Copernicus Meetings, 2023.
[129] LONG Y H, CAO Z W, MAO Y, et al. Research on evaluation elements of urban agricultural green bases: a causal inference-based approach[J]. Land, 2023, 12(8): 1636-1663.
[130] LI Z. PBCLM: a top-down causal modeling framework for soil standards and global sustainable agriculture[J]. Environmental Pollution, 2020, 263: 114404-114418.
[131] GIANNARAKIS G, SITOKONSTANTINOU V, LORILLA R S, et al. Towards assessing agricultural land suitability with causal machine learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 1442-1452.
[132] CALDERON M, LEóN M, Nú?EZ S. Empirical approach to arable land and livestock using co-integration and causality techniques with panel data[C]//Proceedings of the CEUR Workshop, 2021: 44-61.
[133] SI R, AZIZ N, RAZA A. Short and long-run causal effects of agriculture, forestry, and other land use on greenhouse gas emissions: evidence from China using VECM approach[J]. Environmental Science and Pollution Research, 2021, 28: 64419-64430.
[134] COX JR L A. Re-assessing human mortality risks attributed to agricultural air pollution: insights from causal artificial intelligence[M]//AI-ML for decision and risk analysis: cha-
llenges and opportunities for normative decision theory. Berlin: Springer, 2023: 319-350.
[135] LIU T, SHETH P, WEI Y, et al. Causal discovery of agricultural management and reservoir operation induced river water quality change[C]//Proceedings of the AGU Fall Meeting, 2022: H32N-1099.
[136] ZOU L F, ZHA Y Y, DIAO Y Q, et al. Coupling the causal inference and informer networks for short-term forecasting in irrigation water usage[J]. Water Resources Management, 2023, 37(1): 427-449.
[137] SHARMA S, SHARMA S, NEAL A, et al. Causal modeling of soil processes for improved generalization[J]. arXiv:2211.05675, 2022.
[138] KLIANGKHLAO M, LIMSIRORATANA S, SAHOH B. The design and development of a causal bayesian networks model for the explanation of agricultural supply chains[J]. IEEE Access, 2022, 10: 86813-86823.
[139] 陈鎏鹏, 谢帮生, 周子渭, 等. 数字乡村建设是否推动了农村产业融合——基于双重机器学习的因果推断[J]. 金融与经济, 2024, 5(6) : 60-70.
CHEN L P, XIE B S, ZHOU Z W, et al.Whether the construction of digital villages has promoted the integration of rural industries—causal inference based on double machine learning[J]. Finance and Economy, 2024, 5(6): 60-70.
[140] 赵春晖, 宋鹏宇. 从结构推断到根因识别——工业过程故障根因诊断研究综述[J]. 控制与决策, 2023, 38(8): 2130-2157.
ZHAO C H, SONG P Y. From structure inference to root cause identification: a survey on root cause diagnosis of industrial process faults[J]. Control and Decision, 2023, 38(8): 2130-2157.
[141] CHEN X L, WANG J, LIU Q. Distributed system monitoring and fault diagnosis based on causal graphical model[C]//Proceedings of the 2019 1st International Conference on Industrial Artificial Intelligence (IAI), 2019: 1-6.
[142] CHEN X L, YANG Y, WANG J. Plant‐wide processes monitoring and fault tracing based on causal graphical model[J]. IET Control Theory & Applications, 2023(1): 1-13.
[143] DONG J, CAO K R, PENG K X. Hierarchical causal graph-based fault root cause diagnosis and propagation path identification for complex industrial process monitoring[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-11.
[144] QIAO L, LI X T, WANG X, et al. Root cause diagnosis and fault propagation path identification for complex industrial processes based on data space[J]. Measurement, 2024, 226: 114219.
[145] MA L, WANG M W, PENG K X. Nonlinear dynamic granger causality analysis framework for root-cause diagnosis of quality-related faults in manufacturing processes[J]. IEEE Transactions on Automation Science and Engineering, 2023, 21(3): 3554-3563.
[146] OLIVEIRA E E, MIGUéIS V L, BORGES J L. Understanding overlap in automatic root cause analysis in manufacturing using causal inference[J]. IEEE Access, 2021, 10: 191-201.
[147] CHO Y S, KIM S B. Quality-discriminative localization of multisensor signals for root cause analysis[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(7): 4374-4387.
[148] CHEN Q, FEI X Y, XIE L, et al. Causality analysis in process control based on denoising and periodicity-removing CCM[J]. Journal of Intelligent Manufacturing and Special Equipment, 2020, 1(1): 25-41.
[149] SUN Y N, QIN W, ZHUANG Z L, et al. An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window 146 and information geometric causal inference[J]. Journal of Intelligent Manufacturing, 2021, 32: 2007-2021.
[150] XU Z, DANG Y. Automated digital cause-and-effect diagrams to assist causal analysis in problem-solving: a data-driven approach[J]. International Journal of Production Research, 2020, 58(17): 5359-5379.
[151] WANG H N, XU Y M, PENG T, et al. Two-stage approach to causality analysis-based quality problem solving for discrete manufacturing systems[J]. Journal of Engineering Design, 2023(1): 1-25.
[152] LIU Y X, JAFARPOUR B. Graph attention network with Granger causality map for fault detection and root cause diagnosis[J]. Computers & Chemical Engineering, 2024, 180: 108453.
[153] MARTIN M, SUNMOLA F, LAUDER D. Participatory modelling of information technology equipment vulnerability using causal loop analysis[C]//Proceedings of the International Conference on Flexible Automation and Intelligent Manufacturing, 2022: 268-276.
[154] HUA J Q, LI Y G, LIU C Q, et al. A zero-shot prediction method based on causal inference under non-stationary manufacturing environments for complex manufacturing systems[J]. Robotics and Computer-Integrated Manufacturing, 2022, 77: 102356-102365.
[155] LEE T E, VATS S, GIRDHAR S, et al. SCALE: causal learning and discovery of robot manipulation skills using simulation[C]//Proceedings of the 7th Annual Conference on Robot Learning, 2023: 2229-2256.
[156] LEE T E, ZHAO J A, SAWHNEY A S, et al. Causal reasoning in simulation for structure and transfer learning of robot manipulation policies[C]//Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021: 4776-4782.
[157] DIEHL M, RAMIREZ-AMARO K. Why did I fail? a causal-based method to find explanations for robot failures[J]. IEEE Robotics and Automation Letters, 2022, 7(4): 8925-8932.
[158] ZHANG Y, FENG F L, HE X N, et al. Causal intervention for leveraging popularity bias in recommendation[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021: 11-20.
[159] LI Q, WANG X M, WANG Z C, et al. Be causal: de-biasing social network confounding in recommendation[J]. ACM Transactions on Knowledge Discovery from Data, 2023, 17(1): 1-23.
[160] ZHU X Y, ZHANG Y, YANG X, et al. Mitigating hidden confounding effects for causal recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(9): 4794-4805.
[161] HE X N, ZHANG Y, FENG F, et al. Addressing confounding feature issue for causal recommendation[J]. ACM Transactions on Information Systems, 2023, 41(3): 1-23.
[162] NABI R, PFEIFFER J, CHARLES D, et al. Causal inference in the presence of interference in sponsored search advertising[J]. Frontiers in Big Data, 2022, 5: 888592-888604.
[163] WAISMAN C, NAIR H S, CARRION C. Online causal inference for advertising in real-time bidding auctions[J]. arXiv:1908.08600, 2019.
[164] GUI G, NAIR H, NIU F. Auction throttling and causal inference of online advertising effects[J]. arXiv:2112.15155, 2021.
[165] LIU Y, LI G, LIN L. Cross-modal causal relational reasoning for event-level visual question answering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(10): 11624-11641.