Evaluating (weighted) dynamic treatment effects by double machine learning
[Identification of causal effects using instrumental variables]

Citations

Cited by:

1. Phillip Heiler & Michael C. Knaus, 2025. "Heterogeneity Analysis with Heterogeneous Treatments," Papers 2507.01517, arXiv.org.
2. Maria Luisa Maitino & Marco Mariani & Valentina Patacchini & Letizia Ravagli & Nicola Sciclone, 2024. "The Employment Effects of the Italian Minimum Guaranteed Income Scheme Reddito di Cittadinanza," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 10(2), pages 649-681, July.
3. Aleksei Opacic, 2025. "Monotonic Path-Specific Effects: Application to Estimating Educational Returns," Papers 2508.13366, arXiv.org.
4. Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
5. Xu, Aiting & Dai, Yujie & Hu, Zhiyuan & Qiu, Keyang, 2025. "Can green finance policy promote inclusive green growth?- Based on the quasi-natural experiment of China's green finance reform and innovation pilot zone," International Review of Economics & Finance, Elsevier, vol. 100(C).
6. Fabian Muny, 2025. "Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies," Papers 2506.11960, arXiv.org.
7. Xiatian Chen & Kaihua Bao & Chen Gao & Ya Wen & Ting Zhang, 2025. "Towards Corporate Sustainability: Can the Cultural and Tourism Consumption Promotion Policy Enhance Corporate ESG Performance?," Sustainability, MDPI, vol. 17(18), pages 1-28, September.
8. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
9. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
10. Bo Xu & Rengui Sun & Cunhu Xi & Zhaoping Wang, 2025. "Digital governance and the low-carbon transition: evidence from double machine learning," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-14, December.
11. Xiangnan Zhai & Xue Yang & Darko B. Vukovic & Daria A. Dinets & Qiang Liu, 2025. "Carbon Emissions Trading Policy and Regional Energy Efficiency: A Quasi-Natural Experiment from China," Energies, MDPI, vol. 18(5), pages 1-20, February.
12. Shi, Pengfei & Zhang, Honghao & Sun, Chaojing & Wang, Xinrui & Li, Xingming, 2025. "The driving effect of digital finance on green total factor productivity—analysis based on double/debiased machine learning model and spatial durbin model," Finance Research Letters, Elsevier, vol. 81(C).
13. Lin, Junjie, 2025. "Effects of electric vehicle demonstration and promotion policy on air pollution: Evidence from China," Transport Policy, Elsevier, vol. 162(C), pages 1-19.
14. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
15. Shoupeng Wang & Haixin Huang & Fenghua Wu, 2025. "Can Local Industrial Policy Enhance Urban Land Green Use Efficiency? Evidence from the “Made in China 2025” National Demonstration Zone Policy," Land, MDPI, vol. 14(8), pages 1-24, July.
16. Cheuk Hang Leung & Yijun Li & Qi Wu, 2025. "Distribution-valued Causal Machine Learning: Implications of Credit on Spending Patterns," Papers 2509.03063, arXiv.org.
17. Kangqi Jiang & Xiaofeng Chen & Jiayun Li & Mengling Zhou, 2025. "Technology adoption and extreme stock risk: Evidence from digital tax reform in China," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-20, December.
18. Chen, Hongfei & Niu, Dongxiao & Gao, Yibo, 2025. "Research on the impact of energy transition policies on green total factor productivity of Chinese high-energy-consuming enterprises," Energy, Elsevier, vol. 319(C).
19. Lu Kang & Jie Lv & Haoyang Zhang, 2024. "Can the Water Resource Fee-to-Tax Reform Promote the “Three-Wheel Drive” of Corporate Green Energy-Saving Innovations? Quasi-Natural Experimental Evidence from China," Energies, MDPI, vol. 17(12), pages 1-38, June.
20. Xiaofang Dai & Zhenhua Zhang & Weiming Gan & Dongshou Fan, 2025. "Can Digital Economy Facilitate Household Clean Cooking Fuel Transition? Empirical Evidence from China," Sustainability, MDPI, vol. 17(1), pages 1-20, January.
21. Chen, Che & Chen, Song & Wu, Dingwen, 2025. "The impact of ESG performance on R&D investment stability: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 99(C).
22. Chao Wang & Jiapeng Li & Yang Yi & Shuwang Yang, 2024. "Crowding in or crowding out? Executive environmental attention and ESG performance of mining listed companies," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(4), pages 897-913, December.
23. Martin Huber & Kevin Kloiber & Lukas Laffers, 2024. "Testing identification in mediation and dynamic treatment models," Papers 2406.13826, arXiv.org.
24. Ying Ke & Yueqi Wen & Lili Teng, 2025. "Chain Leader Policy and Corporate Environmental Sustainability: A Multi-Level Analysis of Greenwashing Mitigation Mechanisms," Sustainability, MDPI, vol. 17(19), pages 1-45, October.
25. Yaowu, Yang & Qidong, Cheng & Yang, Liu, 2025. "Board structure and corporate strategic aggressiveness - an examination based on the double machine learning method," China Economic Review, Elsevier, vol. 92(C).
26. Fang, Yan & Liu, Yinglin & Yang, Yi & Lucey, Brian & Abedin, Mohammad Zoynul, 2025. "How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning," Research in International Business and Finance, Elsevier, vol. 74(C).