Статті в журналах з теми "Causal machine learning"
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Weiser, Michael, Stefan Feuerriegel, and Tim Herrmann. "Causal Machine Learning." Controlling 32, no. 3 (2020): 86–87. http://dx.doi.org/10.15358/0935-0381-2020-3-86.
Повний текст джерелаWalker, Caren M., Alexandra Rett, and Elizabeth Bonawitz. "Design Drives Discovery in Causal Learning." Psychological Science 31, no. 2 (January 21, 2020): 129–38. http://dx.doi.org/10.1177/0956797619898134.
Повний текст джерелаZhao, Yang, and Qing Liu. "Causal ML: Python package for causal inference machine learning." SoftwareX 21 (February 2023): 101294. http://dx.doi.org/10.1016/j.softx.2022.101294.
Повний текст джерелаGoodman, Steven N., Sharad Goel, and Mark R. Cullen. "Machine Learning, Health Disparities, and Causal Reasoning." Annals of Internal Medicine 169, no. 12 (December 4, 2018): 883. http://dx.doi.org/10.7326/m18-3297.
Повний текст джерелаHuenermund, Paul, Jermain Christopher Kaminski, and Carla Schmitt. "Causal Machine Learning and Business Decision Making." Academy of Management Proceedings 2021, no. 1 (August 2021): 12517. http://dx.doi.org/10.5465/ambpp.2021.12517abstract.
Повний текст джерелаJung, Yonghan, Jin Tian, and Elias Bareinboim. "Estimating Identifiable Causal Effects through Double Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12113–22. http://dx.doi.org/10.1609/aaai.v35i13.17438.
Повний текст джерелаArti, Shindy, Indriana Hidayah, and Sri Suning Kusumawardhani. "Research Trend of Causal Machine Learning Method: A Literature Review." IJID (International Journal on Informatics for Development) 9, no. 2 (December 31, 2020): 111–18. http://dx.doi.org/10.14421/ijid.2020.09208.
Повний текст джерелаSasou, Akira. "Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine." IEEE Access 9 (2021): 165708–18. http://dx.doi.org/10.1109/access.2021.3134700.
Повний текст джерелаZhao, Yiqing, Yue Yu, Hanyin Wang, Yikuan Li, Yu Deng, Guoqian Jiang, and Yuan Luo. "Machine Learning in Causal Inference: Application in Pharmacovigilance." Drug Safety 45, no. 5 (May 2022): 459–76. http://dx.doi.org/10.1007/s40264-022-01155-6.
Повний текст джерелаCrown, William H. "Real-World Evidence, Causal Inference, and Machine Learning." Value in Health 22, no. 5 (May 2019): 587–92. http://dx.doi.org/10.1016/j.jval.2019.03.001.
Повний текст джерелаMarafino, Ben J., Alejandro Schuler, Vincent X. Liu, Gabriel J. Escobar, and Mike Baiocchi. "Predicting preventable hospital readmissions with causal machine learning." Health Services Research 55, no. 6 (October 30, 2020): 993–1002. http://dx.doi.org/10.1111/1475-6773.13586.
Повний текст джерелаWu, Peng, Qi-rui Hu, Xing-wei Tong, and Min Wu. "Learning Causal Effect Using Machine Learning with Application to China’s Typhoon." Acta Mathematicae Applicatae Sinica, English Series 36, no. 3 (July 2020): 702–13. http://dx.doi.org/10.1007/s10255-020-0960-1.
Повний текст джерелаVarian, Hal R. "Causal inference in economics and marketing." Proceedings of the National Academy of Sciences 113, no. 27 (July 5, 2016): 7310–15. http://dx.doi.org/10.1073/pnas.1510479113.
Повний текст джерелаMolina, Mario, and Filiz Garip. "Machine Learning for Sociology." Annual Review of Sociology 45, no. 1 (July 30, 2019): 27–45. http://dx.doi.org/10.1146/annurev-soc-073117-041106.
Повний текст джерелаChernozhukov, Victor, Whitney K. Newey, and Rahul Singh. "Automatic Debiased Machine Learning of Causal and Structural Effects." Econometrica 90, no. 3 (2022): 967–1027. http://dx.doi.org/10.3982/ecta18515.
Повний текст джерелаRathje, Jason Michael, Riitta Katila, and Philipp Reineke. "Causal Inference in Strategy: How can Machine Learning Help?" Academy of Management Proceedings 2021, no. 1 (August 2021): 14156. http://dx.doi.org/10.5465/ambpp.2021.14156abstract.
Повний текст джерелаMoraffah, Raha, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, and Huan Liu. "Causal Interpretability for Machine Learning - Problems, Methods and Evaluation." ACM SIGKDD Explorations Newsletter 22, no. 1 (May 13, 2020): 18–33. http://dx.doi.org/10.1145/3400051.3400058.
Повний текст джерелаLin, Sheng-Hsuan, and Mohammad Arfan Ikram. "On the relationship of machine learning with causal inference." European Journal of Epidemiology 35, no. 2 (September 27, 2019): 183–85. http://dx.doi.org/10.1007/s10654-019-00564-9.
Повний текст джерелаCui, Peng, and Susan Athey. "Stable learning establishes some common ground between causal inference and machine learning." Nature Machine Intelligence 4, no. 2 (February 2022): 110–15. http://dx.doi.org/10.1038/s42256-022-00445-z.
Повний текст джерелаWasserbacher, Helmut, and Martin Spindler. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls." Digital Finance 4, no. 1 (December 16, 2021): 63–88. http://dx.doi.org/10.1007/s42521-021-00046-2.
Повний текст джерелаBrand, Jennie E., Jiahui Xu, Bernard Koch, and Pablo Geraldo. "Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning." Sociological Methodology 51, no. 2 (March 4, 2021): 189–223. http://dx.doi.org/10.1177/0081175021993503.
Повний текст джерелаFei, Nina, Youlong Yang, and Xuying Bai. "One Core Task of Interpretability in Machine Learning — Expansion of Structural Equation Modeling." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 01 (June 6, 2019): 2051001. http://dx.doi.org/10.1142/s0218001420510015.
Повний текст джерелаMarafino, Ben J., Alejandro Schuler, Vincent X. Liu, Gabriel J. Escobar, and Mike Baiocchi. "Correction to “Predicting preventable hospital readmissions with causal machine learning”." Health Services Research 56, no. 1 (January 27, 2021): 168. http://dx.doi.org/10.1111/1475-6773.13611.
Повний текст джерелаKim, Yeji, Taehwa Choi, and Sangbum Choi. "Exploring modern machine learning methods to improve causal-effect estimation." Communications for Statistical Applications and Methods 29, no. 2 (March 31, 2022): 177–91. http://dx.doi.org/10.29220/csam.2022.29.2.177.
Повний текст джерелаMakarov, Ilya, Andrey Savchenko, Arseny Korovko, Leonid Sherstyuk, Nikita Severin, Dmitrii Kiselev, Aleksandr Mikheev, and Dmitrii Babaev. "Temporal network embedding framework with causal anonymous walks representations." PeerJ Computer Science 8 (January 20, 2022): e858. http://dx.doi.org/10.7717/peerj-cs.858.
Повний текст джерелаLin, Fan, Elena Z. Lazarus, and Seung Y. Rhee. "QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants." G3: Genes|Genomes|Genetics 10, no. 7 (May 19, 2020): 2411–21. http://dx.doi.org/10.1534/g3.120.401122.
Повний текст джерелаZeng, Jiaming. "Developing a Machine Learning Tool for Dynamic Cancer Treatment Strategies." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13742–43. http://dx.doi.org/10.1609/aaai.v34i10.7143.
Повний текст джерелаStorm, Hugo, Kathy Baylis, and Thomas Heckelei. "Machine learning in agricultural and applied economics." European Review of Agricultural Economics 47, no. 3 (August 21, 2019): 849–92. http://dx.doi.org/10.1093/erae/jbz033.
Повний текст джерелаLeist, Anja K. "SOCIAL AND BEHAVIORAL FACTORS IN COGNITIVE AGING: APPLYING THE CAUSAL INFERENCE FRAMEWORK IN OBSERVATIONAL STUDIES." Innovation in Aging 3, Supplement_1 (November 2019): S323. http://dx.doi.org/10.1093/geroni/igz038.1178.
Повний текст джерелаSu, Cong, Guoxian Yu, Jun Wang, Zhongmin Yan, and Lizhen Cui. "A review of causality-based fairness machine learning." Intelligence & Robotics 2, no. 3 (2022): 244–74. http://dx.doi.org/10.20517/ir.2022.17.
Повний текст джерелаYao, Liuyi, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. "A Survey on Causal Inference." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (June 26, 2021): 1–46. http://dx.doi.org/10.1145/3444944.
Повний текст джерелаLudwig, Jens, Sendhil Mullainathan, and Jann Spiess. "Augmenting Pre-Analysis Plans with Machine Learning." AEA Papers and Proceedings 109 (May 1, 2019): 71–76. http://dx.doi.org/10.1257/pandp.20191070.
Повний текст джерелаZeng, Zengri, Wei Peng, and Baokang Zhao. "Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning." Security and Communication Networks 2021 (November 3, 2021): 1–18. http://dx.doi.org/10.1155/2021/8986243.
Повний текст джерелаHejazi, Nima, Kara Rudolph, and Iván Díaz. "medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R." Journal of Open Source Software 7, no. 69 (January 5, 2022): 3979. http://dx.doi.org/10.21105/joss.03979.
Повний текст джерелаProsperi, Mattia, Yi Guo, Matt Sperrin, James S. Koopman, Jae S. Min, Xing He, Shannan Rich, Mo Wang, Iain E. Buchan, and Jiang Bian. "Causal inference and counterfactual prediction in machine learning for actionable healthcare." Nature Machine Intelligence 2, no. 7 (July 2020): 369–75. http://dx.doi.org/10.1038/s42256-020-0197-y.
Повний текст джерелаLeng, Siyang, Ziwei Xu, and Huanfei Ma. "Reconstructing directional causal networks with random forest: Causality meeting machine learning." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 9 (September 2019): 093130. http://dx.doi.org/10.1063/1.5120778.
Повний текст джерелаPearl, Judea. "The seven tools of causal inference, with reflections on machine learning." Communications of the ACM 62, no. 3 (February 21, 2019): 54–60. http://dx.doi.org/10.1145/3241036.
Повний текст джерелаJung, Hyekyung. "A case study of estimating a causal effect using machine learning with Bayesian Additive Regression Trees." Korean Society for Educational Evaluation 35, no. 2 (June 30, 2022): 355–77. http://dx.doi.org/10.31158/jeev.2022.35.2.355.
Повний текст джерелаUllal, Mithun S., Iqbal Thonse Hawaldar, Rashmi Soni, and Mohammed Nadeem. "The Role of Machine Learning in Digital Marketing." SAGE Open 11, no. 4 (October 2021): 215824402110503. http://dx.doi.org/10.1177/21582440211050394.
Повний текст джерелаPark, Sung Bae, and Changwon Yoo. "Development of a graphical model of causal gene regulatory networks using medical big data and Bayesian machine learning." Journal of the Korean Medical Association 65, no. 3 (March 10, 2022): 167–72. http://dx.doi.org/10.5124/jkma.2022.65.3.167.
Повний текст джерелаSahoh, Bukhoree, Kanjana Haruehansapong, and Mallika Kliangkhlao. "Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model." IEEE Access 10 (2022): 24327–39. http://dx.doi.org/10.1109/access.2022.3155118.
Повний текст джерелаZivich, Paul N., and Alexander Breskin. "Machine Learning for Causal Inference: On the Use of Cross-fit Estimators." Epidemiology 32, no. 3 (February 2, 2021): 393–401. http://dx.doi.org/10.1097/ede.0000000000001332.
Повний текст джерелаGraña, Manuel, Leire Ozaeta, and Darya Chyzhyk. "Dynamic Causal Modeling and machine learning for effective connectivity in Auditory Hallucination." Neurocomputing 326-327 (January 2019): 61–68. http://dx.doi.org/10.1016/j.neucom.2016.08.157.
Повний текст джерелаChoi, Byeong Yeob, Chen‐Pin Wang, and Jonathan Gelfond. "Machine learning outcome regression improves doubly robust estimation of average causal effects." Pharmacoepidemiology and Drug Safety 29, no. 9 (July 27, 2020): 1120–33. http://dx.doi.org/10.1002/pds.5074.
Повний текст джерелаChen, Jau-er, Chien-Hsun Huang, and Jia-Jyun Tien. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions." Econometrics 9, no. 2 (April 2, 2021): 15. http://dx.doi.org/10.3390/econometrics9020015.
Повний текст джерелаChalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review 106, no. 5 (May 1, 2016): 124–27. http://dx.doi.org/10.1257/aer.p20161029.
Повний текст джерелаAthey, Susan, and Guido W. Imbens. "Machine Learning Methods That Economists Should Know About." Annual Review of Economics 11, no. 1 (August 2, 2019): 685–725. http://dx.doi.org/10.1146/annurev-economics-080217-053433.
Повний текст джерелаZhou, Wusheng. "Prediction of Urban and Rural Tourism Economic Forecast Based on Machine Learning." Scientific Programming 2021 (September 22, 2021): 1–7. http://dx.doi.org/10.1155/2021/4072499.
Повний текст джерелаLangen, Henrika, and Martin Huber. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign." PLOS ONE 18, no. 1 (January 11, 2023): e0278937. http://dx.doi.org/10.1371/journal.pone.0278937.
Повний текст джерелаLemmens, Aurélie, and Sunil Gupta. "Managing Churn to Maximize Profits." Marketing Science 39, no. 5 (September 2020): 956–73. http://dx.doi.org/10.1287/mksc.2020.1229.
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