Journal articles on the topic 'Debiased machine learning'
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Ahrens, Achim, Christian B. Hansen, Mark E. Schaffer, and Thomas Wiemann. "ddml: Double/debiased machine learning in Stata." Stata Journal: Promoting communications on statistics and Stata 24, no. 1 (March 2024): 3–45. http://dx.doi.org/10.1177/1536867x241233641.
Full textChernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey. "Double/Debiased/Neyman Machine Learning of Treatment Effects." American Economic Review 107, no. 5 (May 1, 2017): 261–65. http://dx.doi.org/10.1257/aer.p20171038.
Full textChen, 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.
Full textChernozhukov, 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.
Full textLiu, Molei, Yi Zhang, and Doudou Zhou. "Double/debiased machine learning for logistic partially linear model." Econometrics Journal 24, no. 3 (June 11, 2021): 559–88. http://dx.doi.org/10.1093/ectj/utab019.
Full textChernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. "Double/debiased machine learning for treatment and structural parameters." Econometrics Journal 21, no. 1 (January 16, 2018): C1—C68. http://dx.doi.org/10.1111/ectj.12097.
Full textChang, Neng-Chieh. "Double/debiased machine learning for difference-in-differences models." Econometrics Journal 23, no. 2 (February 4, 2020): 177–91. http://dx.doi.org/10.1093/ectj/utaa001.
Full textFu, Runshan, Yan Huang, and Param Vir Singh. "Crowds, Lending, Machine, and Bias." Information Systems Research 32, no. 1 (March 1, 2021): 72–92. http://dx.doi.org/10.1287/isre.2020.0990.
Full textJung, 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.
Full textTsai, Yun-Da, Cayon Liow, Yin Sheng Siang, and Shou-De Lin. "Toward More Generalized Malicious URL Detection Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (March 24, 2024): 21628–36. http://dx.doi.org/10.1609/aaai.v38i19.30161.
Full textHridi, Anurata Prabha, Rajeev Sahay, Seyyedali Hosseinalipour, and Bita Akram. "Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems." Proceedings of the AAAI Symposium Series 3, no. 1 (May 20, 2024): 297–303. http://dx.doi.org/10.1609/aaaiss.v3i1.31217.
Full textJiang, Xiangxiang, Gang Lv, Minghui Li, and Kevin Lu. "CAUSAL EFFECT OF ANTI-DEMENTIA DRUGS ON PATIENTS’ ECONOMIC BURDEN USING DOUBLE/DEBIASED MACHINE LEARNING APPROACH." Innovation in Aging 7, Supplement_1 (December 1, 2023): 1000. http://dx.doi.org/10.1093/geroni/igad104.3213.
Full textWei, Rongrong, and Yueming Xia. "Digital transformation and corporate green total factor productivity: Based on double/debiased machine learning robustness estimation." Economic Analysis and Policy 84 (December 2024): 808–27. http://dx.doi.org/10.1016/j.eap.2024.09.023.
Full textChernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, and James M. Robins. "Locally Robust Semiparametric Estimation." Econometrica 90, no. 4 (2022): 1501–35. http://dx.doi.org/10.3982/ecta16294.
Full textZhang, Yingheng, Haojie Li, and Gang Ren. "Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach." Transportation Research Part A: Policy and Practice 163 (September 2022): 288–303. http://dx.doi.org/10.1016/j.tra.2022.07.015.
Full textGuo, Yaming, Meng Li, Keqiang Li, Huiping Li, and Yunxuan Li. "Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning." Accident Analysis & Prevention 208 (December 2024): 107806. http://dx.doi.org/10.1016/j.aap.2024.107806.
Full textIchimura, Hidehiko, and Whitney K. Newey. "The influence function of semiparametric estimators." Quantitative Economics 13, no. 1 (2022): 29–61. http://dx.doi.org/10.3982/qe826.
Full textZhang, Guidong, Jianlong Wang, and Yong Liu. "How does auditing outgoing officials' natural resource asset management policies affect carbon emission efficiency? Evidence from a debiased machine learning model." Journal of Cleaner Production 478 (November 2024): 143932. http://dx.doi.org/10.1016/j.jclepro.2024.143932.
Full text包, 娣娜. "The Impact of Carbon Emission Trading on the Environmental Performance of Emission Control Enterprises—Based on Double/Debiased Machine Learning Model." Journal of Low Carbon Economy 13, no. 04 (2024): 276–84. http://dx.doi.org/10.12677/jlce.2024.134027.
Full textZhang, Qinghua, Yuhang Chen, Yilin Zhong, and Junhao Zhong. "The positive effects of the higher education expansion policy on urban innovation in China." AIMS Mathematics 9, no. 2 (2024): 2985–3010. http://dx.doi.org/10.3934/math.2024147.
Full textGirma, Sourafel, and David Paton. "Using double-debiased machine learning to estimate the impact of Covid-19 vaccination on mortality and staff absences in elderly care homes." European Economic Review 170 (November 2024): 104882. http://dx.doi.org/10.1016/j.euroecorev.2024.104882.
Full textNguyen-Phung, Hang Thu, and Hai Le. "Energy Poverty and Health Expenditure: Empirical Evidence from Vietnam." Social Sciences 13, no. 5 (May 6, 2024): 253. http://dx.doi.org/10.3390/socsci13050253.
Full textOlivares, Barlin O., Juan C. Rey, Guillermo Perichi, and Deyanira Lobo. "Relationship of Microbial Activity with Soil Properties in Banana Plantations in Venezuela." Sustainability 14, no. 20 (October 19, 2022): 13531. http://dx.doi.org/10.3390/su142013531.
Full textLi, Hairui, Xuemei Liu, Xiaolu Chen, and Xianfeng Huai. "Robust Anomaly Recognition in Hydraulic Structural Safety Monitoring: A Methodology Based on Deconfounding Boosted Regression Trees." Mathematical Problems in Engineering 2023 (August 16, 2023): 1–15. http://dx.doi.org/10.1155/2023/7854792.
Full textLi, Enji, Qing Chen, Xinyan Zhang, and Chen Zhang. "Digital Government Development, Local Governments’ Attention Distribution and Enterprise Total Factor Productivity: Evidence from China." Sustainability 15, no. 3 (January 30, 2023): 2472. http://dx.doi.org/10.3390/su15032472.
Full textPinkava, Thomas, Jack McFarland, and Afra Mashhadi. "A Model- and Data-Agnostic Debiasing System for Achieving Equalized Odds." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 1123–31. http://dx.doi.org/10.1609/aies.v7i1.31709.
Full textChowdhury, Somnath Basu Roy, and Snigdha Chaturvedi. "Learning Fair Representations via Rate-Distortion Maximization." Transactions of the Association for Computational Linguistics 10 (2022): 1159–74. http://dx.doi.org/10.1162/tacl_a_00512.
Full textIslam, Rashidul, Huiyuan Chen, and Yiwei Cai. "Fairness without Demographics through Shared Latent Space-Based Debiasing." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12717–25. http://dx.doi.org/10.1609/aaai.v38i11.29167.
Full textRana, Saadia Afzal, Zati Hakim Azizul, and Ali Afzal Awan. "A step toward building a unified framework for managing AI bias." PeerJ Computer Science 9 (October 26, 2023): e1630. http://dx.doi.org/10.7717/peerj-cs.1630.
Full textMaasoumi, Esfandiar Essie, Jianqiu Wang, Zhuo Wang, and Ke Wu. "Identifying Factors via Automatic Debiased Machine Learning." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4223091.
Full textChiang, Harold D., Kengo Kato, Yukun Ma, and Yuya Sasaki. "Multiway Cluster Robust Double/Debiased Machine Learning." Journal of Business & Economic Statistics, April 19, 2021, 1–11. http://dx.doi.org/10.1080/07350015.2021.1895815.
Full textAhrens, Achim, Christian Hansen, Mark Schaffer, and Thomas T. Wiemann. "Ddml: Double/Debiased Machine Learning in Stata." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4368837.
Full textMaasoumi, Esfandiar, Jianqiu Wang, Zhuo Wang, and Ke Wu. "Identifying factors via automatic debiased machine learning." Journal of Applied Econometrics, February 13, 2024. http://dx.doi.org/10.1002/jae.3031.
Full textSemenova, Vira. "Debiased machine learning of set-identified linear models." Journal of Econometrics, January 2023. http://dx.doi.org/10.1016/j.jeconom.2022.12.010.
Full textChernozhukov, V., W. K. Newey, and R. Singh. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees." Biometrika, June 14, 2022. http://dx.doi.org/10.1093/biomet/asac033.
Full textDai, Xiaowu, and Lexin Li. "Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis." Journal of the American Statistical Association, February 3, 2022, 1–15. http://dx.doi.org/10.1080/01621459.2021.2013851.
Full textKim, Gene Hyungjin. "Double/Debiased Machine Learning for Static Games with Incomplete Information." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4377695.
Full textSemenova, Vira, and Victor Chernozhukov. "Debiased machine learning of conditional average treatment effects and other causal functions." Econometrics Journal, August 29, 2020. http://dx.doi.org/10.1093/ectj/utaa027.
Full textJin, Zequn, Lihua Lin, and Zhengyu Zhang. "Identification and Auto-debiased Machine Learning for Outcome-Conditioned Average Structural Derivatives." Journal of Business & Economic Statistics, January 24, 2024, 1–27. http://dx.doi.org/10.1080/07350015.2024.2310022.
Full textShi, Bowen, Xiaojie Mao, Mochen Yang, and Bo Li. "What, Why, and How: An Empiricist's Guide to Double/Debiased Machine Learning." SSRN Electronic Journal, 2024. http://dx.doi.org/10.2139/ssrn.4677153.
Full textDíaz, Iván. "Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning." Biostatistics, November 19, 2019. http://dx.doi.org/10.1093/biostatistics/kxz042.
Full textMoccia, Chiara, Giovenale Moirano, Maja Popovic, Costanza Pizzi, Piero Fariselli, Lorenzo Richiardi, Claus Thorn Ekstrøm, and Milena Maule. "Machine learning in causal inference for epidemiology." European Journal of Epidemiology, November 13, 2024. http://dx.doi.org/10.1007/s10654-024-01173-x.
Full textFeyzollahi, Maryam, and Nima Rafizadeh. "Double/Debiased Machine Learning for Economists: Practical Guidelines, Best Practices, and Common Pitfalls." SSRN Electronic Journal, 2024. http://dx.doi.org/10.2139/ssrn.4703243.
Full textBhagavathula, Akshaya Srikanth. "Causal Analysis of PFAS Exposure and Cardiovascular Risk Using Double/Debiased Machine Learning." Annals of Epidemiology, August 2024. http://dx.doi.org/10.1016/j.annepidem.2024.07.050.
Full textKabata, Daijiro, and Mototsugu Shintani. "Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach." Communications in Statistics - Simulation and Computation, November 15, 2021, 1–14. http://dx.doi.org/10.1080/03610918.2021.2001655.
Full textLoecher, Markus. "Debiasing SHAP scores in random forests." AStA Advances in Statistical Analysis, August 22, 2023. http://dx.doi.org/10.1007/s10182-023-00479-7.
Full textWang, Clarice, Kathryn Wang, Andrew Y. Bian, Rashidul Islam, Kamrun Naher Keya, James Foulds, and Shimei Pan. "When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation." ACM Transactions on Interactive Intelligent Systems, August 2023. http://dx.doi.org/10.1145/3611313.
Full textZhong, Junhao, Zhenzhen Wang, and Yunfeng Deng. "Driving the Green Transformation of Enterprises: The Role of Patent Insurance." Managerial and Decision Economics, November 21, 2024. http://dx.doi.org/10.1002/mde.4439.
Full textKamal, Kimia, and Bilal Farooq. "Debiased Machine Learning for Estimating the Causal Effect of Urban Traffic on Pedestrian Crossing Behavior." Transportation Research Record: Journal of the Transportation Research Board, February 8, 2023, 036119812311522. http://dx.doi.org/10.1177/03611981231152246.
Full textKovács, Dávid Péter, William McCorkindale, and Alpha A. Lee. "Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias." Nature Communications 12, no. 1 (March 16, 2021). http://dx.doi.org/10.1038/s41467-021-21895-w.
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