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The Blessings of Multiple Causes: Rejoinder

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  • Yixin Wang
  • David M. Blei

Abstract

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Suggested Citation

  • Yixin Wang & David M. Blei, 2019. "The Blessings of Multiple Causes: Rejoinder," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1616-1619, October.
    • Handle: RePEc:taf:jnlasa:v:114:y:2019:i:528:p:1616-1619
    DOI: 10.1080/01621459.2019.1690841
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    Citations

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    Cited by:

    1. Cheng Zheng & Lei Liu, 2022. "Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach," Biometrics, The International Biometric Society, vol. 78(3), pages 1233-1243, September.
    2. Tsionas, Mike G. & Patel, Pankaj C., 2023. "Tinkering or orchestrating? The value of country-level asset management capability and entrepreneurship outcomes," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Bernard Koch & Tim Sainburg & Pablo Geraldo & Song Jiang & Yizhou Sun & Jacob Gates Foster, 2021. "A Primer on Deep Learning for Causal Inference," Papers 2110.04442, arXiv.org, revised Nov 2023.
    4. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    5. Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021. "Deep Learning of Potential Outcomes," SocArXiv aeszf, Center for Open Science.
    6. Christian Stetter & Philipp Mennig & Johannes Sauer, 2022. "Using Machine Learning to Identify Heterogeneous Impacts of Agri-Environment Schemes in the EU: A Case Study," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 723-759.
    7. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
    9. Zhang, Xiaoke & Xue, Wu & Wang, Qiyue, 2021. "Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    10. Pengzhou Wu & Kenji Fukumizu, 2021. "Towards Principled Causal Effect Estimation by Deep Identifiable Models," Papers 2109.15062, arXiv.org, revised Nov 2021.
    11. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2024. "The impacts of innovation and trade openness on bank market power: The proposal of a minimum distance cost function approach and a causal structure analysis," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1178-1194.

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