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Big Data and Causality

Author

Listed:
  • Hossein Hassani

    (Institute for International Energy Studies)

  • Xu Huang

    (De Montfort University)

  • Mansi Ghodsi

    (Institute for International Energy Studies)

Abstract

Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory.

Suggested Citation

  • Hossein Hassani & Xu Huang & Mansi Ghodsi, 2018. "Big Data and Causality," Annals of Data Science, Springer, vol. 5(2), pages 133-156, June.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:2:d:10.1007_s40745-017-0122-3
    DOI: 10.1007/s40745-017-0122-3
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    References listed on IDEAS

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    1. William W. Chow & Michael K. Fung, 2013. "Financial development and growth: A clustering and causality analysis," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 22(3), pages 430-453, April.
    2. Hassani, Hossein & Huang, Xu & Gupta, Rangan & Ghodsi, Mansi, 2016. "Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 54-65.
    3. Christian Bizer & Tom Heath & Tim Berners-Lee, 2009. "Linked Data - The Story So Far," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 5(3), pages 1-22, July.
    4. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    5. Soytas, Ugur & Sari, Ramazan, 2003. "Energy consumption and GDP: causality relationship in G-7 countries and emerging markets," Energy Economics, Elsevier, vol. 25(1), pages 33-37, January.
    6. Ale, B.J.M. & Bellamy, L.J. & van der Boom, R. & Cooper, J. & Cooke, R.M. & Goossens, L.H.J. & Hale, A.R. & Kurowicka, D. & Morales, O. & Roelen, A.L.C. & Spouge, J., 2009. "Further development of a Causal model for Air Transport Safety (CATS): Building the mathematical heart," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1433-1441.
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    Cited by:

    1. Guillaume Wunsch & Federica Russo & Michel Mouchart & Renzo Orsi, 2020. "Time and Causality in the Social Sciences," Working Papers wp1155, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Emmanuel Afuecheta & Chigozie Utazi & Edmore Ranganai & Chibuzor Nnanatu, 2023. "An Application of Extreme Value Theory for Measuring Financial Risk in BRICS Economies," Annals of Data Science, Springer, vol. 10(2), pages 251-290, April.
    3. Sanjay Kumar, 2020. "Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis," Annals of Data Science, Springer, vol. 7(3), pages 417-425, September.
    4. Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    5. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.

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