Causal inference-integrated temporal graph convolutional networks for dynamic prediction and optimization of enterprise total factor productivity
- DOI
- 10.1038/s41598-026-57633-9
- Published
- 2026-06-12
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-57633-9,
title = {Causal inference-integrated temporal graph convolutional networks for dynamic prediction and optimization of enterprise total factor productivity},
author = {Gaoyuan Fu},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-57633-9},
url = {https://doi.org/10.1038/s41598-026-57633-9}
}RIS
TY - JOUR TI - Causal inference-integrated temporal graph convolutional networks for dynamic prediction and optimization of enterprise total factor productivity AU - Gaoyuan Fu PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-57633-9 UR - https://doi.org/10.1038/s41598-026-57633-9 ER -
APA
Fu, G. (2026). Causal inference-integrated temporal graph convolutional networks for dynamic prediction and optimization of enterprise total factor productivity. Scientific Reports. https://doi.org/10.1038/s41598-026-57633-9
Source records
- crossref · retrieved 2026-09-25T12:31:49.994Z