Zobrazeno 1 - 10
of 1 244
pro vyhledávání: '"Double machine learning"'
Autor:
Wang, Xuqing1 (AUTHOR), Liu, Yahang1 (AUTHOR), Qin, Guoyou1,2 (AUTHOR) gyqin@fudan.edu.cn, Yu, Yongfu1,2 (AUTHOR) yu@fudan.edu.cn
Publikováno v:
BMC Bioinformatics. 11/14/2024, Vol. 25 Issue 1, p1-19. 19p.
Autor:
Aouar, Lynda, Yu, Han
Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS), a subsampling m
Externí odkaz:
http://arxiv.org/abs/2411.14665
Publikováno v:
Lecture Notes in Computer Science, vol 14174. (2023) Springer, Cham
Causal Impact (CI) of customer actions are broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate the CI values acr
Externí odkaz:
http://arxiv.org/abs/2409.02332
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-19 (2024)
Abstract Background Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their
Externí odkaz:
https://doaj.org/article/c1a6b006f87c4fa3a18c55916744317f
Autor:
Fuhr, Jonathan, Papies, Dominik
Estimating causal effect using machine learning (ML) algorithms can help to relax functional form assumptions if used within appropriate frameworks. However, most of these frameworks assume settings with cross-sectional data, whereas researchers ofte
Externí odkaz:
http://arxiv.org/abs/2409.01266
Autor:
Chen, Xiaoyu a, b, Wang, Haohan a, b, ⁎
Publikováno v:
In Journal of Environmental Management November 2024 370
Autor:
Fang, Yan a, 1, Liu, Yinglin b, ⁎, 1, Yang, Yi c, 1, Lucey, Brian d, 1, Abedin, Mohammad Zoynul e, 1
Publikováno v:
In Research in International Business and Finance February 2025 74
A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable kn
Externí odkaz:
http://arxiv.org/abs/2405.08498
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