FROM ECONOMETRICS TO CAUSAL REASONING: ARTIFICIAL INTELLIGENCE AND ECONOMIC EDUCATION IN THE AGE OF BIG DATA
DOI:
https://doi.org/10.37135/kai.03.16.10Keywords:
Artificial intelligence, Big Data, Causal inference, Empirical credibility, Applied econometrics, Economics educationAbstract
The rapid expansion of artificial intelligence (AI) and Big Data is transforming both the labor market and economic research, intensifying concerns about the credibility of empirical results, the emphasis on causality, and current university training. This article examines these changes in the context of the credibility revolution in empirical economics, highlighting its contributions to research design as well as its limitations when addressing complex and dynamic systems. Based on a critical review of recent literature, it argues that larger datasets and greater computational power do not ensure better inference without appropriate conceptual frameworks. The article proposes a methodological integration of design-based approaches, structural causal models, and machine learning techniques, and discusses the implications of this convergence for economics education and future economists’ employability in the AI era.
Downloads
References
Acemoglu, D. (2024). The simple macroeconomics of AI. Economic Policy, 40(121), 13–58. https://doi.org/10.1093/epolic/eiae042
Angrist, J. D., & Pischke, J.-S. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24(2), 3–30. https://doi.org/10.1257/jep.24.2.3
Athey, S., & Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3–32. https://doi.org/10.1257/jep.31.2.3
Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685–725. https://doi.org/10.1146/annurev-economics-080217-053433
Baqaee, D. R., & Farhi, E. (2019a). Macroeconomics with heterogeneous agents and input–output networks (NBER Working Paper No. 24684). National Bureau of Economic Research.
Baqaee, D. R., & Farhi, E. (2019b). The macroeconomic impact of microeconomic shocks: Beyond Hulten’s theorem. Econometrica, 87(4), 1155–1203. https://doi.org/10.3982/ECTA15202
Baqaee, D. R., & Farhi, E. (2019c). The microeconomic foundations of aggregate production functions (NBER Working Paper No. 25293). National Bureau of Economic Research.
Baqaee, D. R., & Farhi, E. (2021). Networks, barriers, and trade (NBER Working Paper No. 26108). National Bureau of Economic Research.
Bareinboim, E., & Pearl, J. (2016). Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences, 113(27), 7345–7352. https://doi.org/10.1073/pnas.1510507113
Bartoš, F., Maier, M., Wagenmakers, E.-J., Nippold, F., Doucouliagos, H., Ioannidis, J. P. A., Otte, W. M., Sladekova, M., Deressa, T. K., Bruns, S. B., Fanelli, D., & Stanley, T. (2024). Footprint of publication selection bias on meta-analyses in medicine, environmental sciences, psychology, and economics. Research Synthesis Methods. https://doi.org/10.1002/jrsm.1703
Bongers, S., Peters, J., Mooij, J. M., & Schölkopf, B. (2021). Foundations of structural causal models with cycles and latent variables. Annals of Statistics, 49(5), 2885–2915. https://doi.org/10.1214/20-AOS2017
Briggs, J., & Kodnani, D. (2023). Generative AI could raise global GDP by 7%. Goldman Sachs Research. https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.
https://doi.org/10.1111/ectj.12097
Christensen, G., & Miguel, E. (2018). Transparency, reproducibility, and the credibility of economics research. Journal of Economic Literature, 56(3), 920–980.
https://doi.org/10.1257/jel.20171350
CNN Business. (2025, 29 mayo). Why this leading AI CEO is warning the tech could cause mass unemployment. https://edition.cnn.com/2025/05/29/tech/ai-anthropic-ceo-dario-amodei-unemployment
Couldry, N., & Mejías, U. A. (2019). The costs of connection: How data colonizes human life and appropriates it for capitalism. Stanford University Press.
Cukier, K., & Mayer-Schönberger, V. (2017). Big data: A revolution that will transform how we live, work, and think (Rev. ed.). Eamon Dolan/Mariner Books.
Deaton, A. (2010). Instruments, randomization, and learning about development. Journal of Economic Literature, 48(2), 424–455. https://doi.org/10.1257/jel.48.2.424
Deaton, A., & Cartwright, N. (2018). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine, 210, 2–21. https://doi.org/10.1016/j.socscimed.2017.12.005
Federal Reserve Bank of New York. (2025).
The labor market for recent college graduates.
https://www.newyorkfed.org/research/college-labor-market
Federal Reserve Bank of St. Louis. (2025, August 25).
Recent college graduates bear brunt of labor market shifts.
Ferraro, P. J., & Shukla, P. (2020). Is a replicability crisis on the horizon for environmental and resource economics? Review of Environmental Economics and Policy, 14(2), 339–351. https://doi.org/10.1093/reep/reaa011
Forré, P., Mooij, J. M., & Peters, J. (2022). Causal discovery in the presence of cycles. Journal of Machine Learning Research, 23(146), 1–53.
https://www.jmlr.org/papers/v23/21-031.html
Fortune. (2025, 22 julio). “Intelligence too cheap to meter” is AI’s next frontier, Sam Altman says. https://fortune.com/2025/07/23/sam-altman-artificial-intelligence-too-cheap-jobs/
Gechert, S., Rannenberg, A., & Watzka, S. (2023). Fiscal multipliers, macroeconomic conditions, and publication bias. Journal of Economic Surveys, 37(2), 307–339. https://doi.org/10.1111/joes.12503
Ghassami, A., Zhang, K., & Schölkopf, B. (2024). Causal discovery in feedback systems: Methods and challenges. Foundations and Trends® in Machine Learning.
Heckman, J. J., & Urzúa, S. (2010). Comparing IV with structural models: What simple IV can and cannot identify. Journal of Econometrics, 156(1), 27–37.
https://doi.org/10.1016/j.jeconom.2009.09.007
Hosseini, S. M., & Lichtinger, G. (2025). Generative AI as seniority-biased technological change: Evidence from U.S. résumé and job posting data (SSRN Working Paper No. 5425555). https://doi.org/10.2139/ssrn.5425555
Hünermund, P., & Bareinboim, E. (2023). Causal inference and data fusion in econometrics. The Econometrics Journal, 26(1), S1–S28. https://doi.org/10.1093/ectj/utac031
IDC. (2024). The digitization of the world: From edge to core (Updated forecast 2023–2027). International Data Corporation.
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124
Ioannidis, J. P. A. (2020). The challenge of reforming nutritional epidemiologic research. JAMA, 324(6), 559–560. https://doi.org/10.1001/jama.2020.11028
Kamaruddin, R., Ahmad, A., & Mansor, N. (2023). Employability skills and labor market mismatch: Evidence from higher education graduates. Journal of Education and Work, 36(3), 247–263. https://doi.org/10.1080/13639080.2023.2183579
Kitchin, R. (2021). Data lives: How data are made and shape our world. Bristol University Press.
Mooij, J. M., Janzing, D., & Schölkopf, B. (2024). Causal discovery for time series and dynamical systems. Annual Review of Statistics and Its Application, 11, 1–26. https://doi.org/10.1146/annurev-statistics-033121-113547
OECD. (2023). OECD skills outlook 2023: Skills for a resilient green and digital transition. OECD Publishing. https://doi.org/10.1787/27452b1c-en
Page, L., Noussair, C. N., & Slonim, R. (2021). The credibility crisis in economics: The importance of replication and pre-analysis plans. Oxford Economic Papers, 73(4), 1057–1084. https://doi.org/10.1093/oep/gpab022
Pearl, J. (2019). The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3), 54–60. https://doi.org/10.1145/3241036
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.
Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference: Foundations and learning algorithms. MIT Press.
Portocarrero Ramos, J., Ruiz, K., & Alvarado, C. (2025). University training, skills mismatch, and youth employability in Latin America. Journal of Labor and Society. Advance online publication.
Ribeiro, M. T., Saxena, N. A., & Lundberg, S. (2022). Challenges of explaining AI models when critical contextual variables are missing. Nature Machine Intelligence, 4, 673–685. https://doi.org/10.1038/s42256-022-00532-0
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE, 109(5), 612–634. https://doi.org/10.1109/JPROC.2021.3058954
Segbenya, M., Kpessa-Whyte, M., & Teye, J. K. (2023). Graduate employability and skills mismatch in emerging economies. International Journal of Educational Development, 96, 102702. https://doi.org/10.1016/j.ijedudev.2022.102702
Strømland, E. A. (2019). Publication bias and selective reporting in experimental economics. Journal of Economic Behavior & Organization, 163, 252–268.
https://doi.org/10.1016/j.jebo.2019.05.006
Taylor, L., Floridi, L., & van der Sloot, B. (Eds.). (2020).
Group privacy: New challenges of data technologies. Springer.
van der Zee, T., & Reichardt, J. (2021). The reproducibility of data-driven research: A review of the state of the art and future perspectives. Royal Society Open Science, 8(12), 210966. https://doi.org/10.1098/rsos.210966
Wagner, C., & Cooper, G. (2023). Simpson’s paradox in machine learning: Implications for fairness and model evaluation. Patterns, 4(7), 100779.
https://doi.org/10.1016/j.patter.2023.100779
Wilson, D. S., & Wilson, E. O. (2007). Rethinking the theoretical foundation of sociobiology. Quarterly Review of Biology, 82(4), 327–348. https://doi.org/10.1086/522809
Wut, T. M., Xu, J., & Wong, S. M. (2024). Skills mismatch, automation risk, and graduate employability in the digital economy. Technological Forecasting and Social Change, 198, 122036. https://doi.org/10.1016/j.techfore.2023.122036
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kairos: Journal of Economy, Law and Administrative Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright
By submitting his work to Kairós, Journal of economic, law and administrative sciences, the author assigns the editor in a non-exclusive manner the rights of reproduction, publication, public communication, distribution and transformation so that it can be published in the journal in electronic version and can be consulted from the magazine's website.
Likewise, the authors authorize their article to be published under a Creative Commons Attribution CC BY-NC-ND license.
The authors who publish in this journal agree to the following terms:
The authors retain the copyright and guarantee the journal the right to be the first publication of the work as well as licensed under a Creative Commons Attribution CC BY-NC-ND license.
Authors may separately establish additional agreements for the non-exclusive distribution of the version of the work published in the journal (for example, placing it in an institutional repository or publishing it in a book), with an acknowledgment of its initial publication in this journal.
Authors are allowed and encouraged to post their work electronically (for example, in institutional repositories or on their own website) after publication, as it can lead to productive exchanges, as well as earlier and greater citation. of published works (See The Effect of Open Access).
![Manual de marca](https://i.ibb.co/8mRCcNn/logo-para-manual-de-marca-Kair-s.png"){kind=logo}
