2025 2(49) 20

Roadmap for top managers from descriptive statistics to causal management

Tkachenko A. M.,
Doctor of Economics, Professor,
ORCID https://orcid.org/0000-0002-1843-2579
е-mail: alla0676128584@gmail.com
National University “Zaporizhzhia Polytechnic”, Zaporizhzhia
Chornyi R. S.,
Doctor of Economics, Professor, Director,
ORCID https://orcid.org/0000-0001-8614-9495
Novovolynsk Educational and Scientific Institute of Economics and Management, Novovolynsk

Citation Format
Tkachenko, A. M., & Chornyi, R. S. (2025). Roadmap for top managers from descriptive statistics to causal management. Vіsnyk ekonomіchnoі nauky Ukraіny, 2(49), 180-190. https://doi.org/10.37405/1729-7206.2025.2(49).180-190 

Language
Ukrainian

Resume
Over the past decade, corporate analytics systems have rapidly evolved from manual reporting to scalable business intelligence platforms with advanced dashboards, as well as predictive and recommendation models. As a result, a significant portion of management decisions continue to rely on correlations that rarely answer the key question: what will happen if we intervene and modify pricing policy, inventory levels, technology, training programmes, marketing communications, or institutional rules? The lack of an operational bridge between ‘description/forecast’ and “causality” creates systemic risks: false attributions, scaling of ‘false victories,’ overconfidence in short-term correlations, and chronic underinvestment in interventions with high expected value but with a weak historical track record in the data. Therefore, there is a growing need for a roadmap that moves the firm from a ‘watch and guess’ mode to a mode of managed causality.

Keywords
causal management, causal DAGs, DiD (difference-in-ifferences), SCM (synthetic control), IV (instrumental variables), RDD (regression discontinuity), CATE (conditional average treatment effect), EPV (expected profit value), VOI (value of information), ExO (experimental office).

Referensces

  1. Baker, A., et al. (2025). Difference-in-Differences Designs: A Practitioner’s Guide. arXiv preprint arXiv:2503.13323. https://arxiv.org/abs/2503.13323
  2. Caetano, C., & Callaway, B. (2024). Difference-in-Differences when Parallel Trends Holds Conditional on Covariates. Working paper. https://bcallaway11.github.io/files/DID-Covariates/Caetano_Callaway_2024.pdf
  3. Camuffo, A., Gambardella, A., Messinese, D., Novelli, E., Paolucci, E., & Spina, C. (2024). A scientific approach to entrepreneurial decision-making: Large-scale replication and extension. Strategic Management Journal, 45(6), 1209–1237. https://doi.org/10.1002/smj.3580
  4. Leonelli, S., Muhn, M., Rauter, T., & Sran, G. (2024). How Do Consumers Use Firm Disclosure? Evidence from a Randomized Field Experiment (BFI Working Paper No. 2024-04). Becker Friedman Institute. https://doi.org/10.2139/ssrn.4687694
  5. Ben-Michael, E., Feller, A., & Rothstein, J. (2022). Synthetic controls with staggered adoption. Journal of the Royal Statistical Society: Series B, 84(2), 351–381. https://doi.org/10.1111/rssb.12448
  6. Hernan, M. A., & Robins, J. M. (2024, January 2). Causal Inference: What If. Harvard T.H. Chan School of Public Health. https://content.sph.harvard.edu/wwwhsph/sites/1268/2024/01/hernanrobins_WhatIf_2jan24.pdf
  7. Imbens, G. W. (2024). Causal inference in the social sciences. Annual Review of Statistics and Its Application, 11, 123–152. https://doi.org/10.1146/annurev-statistics-033121-114601
  8. Dahabreh, I. J., & Bibbins-Domingo, K. (2024). Causal inference about the effects of interventions from observational studies in medical journals. JAMA, 331(21), 1845–1853. https://doi.org/10.1001/jama.2024.7741
  9. Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391–425. https://doi.org/10.1257/jel.20191450
  10. Mogstad, M., & Torgovitsky, A. (2024). Instrumental variables with unobserved heterogeneity in treatment effects. Handbook of Labor Economics, 5, 1–114. Elsevier. https://doi.org/10.3386/w32927
  11. Callaway, B., & Sant’Anna, P. H. C. (2024). Difference-in-Differences with multiple time periods (vignette, updated September 10, 2024). https://bcallaway11.github.io/did/articles/did-basics.html
  12. Rehill, P., Gronsbell, J., & van der Laan, M. J. (2025). How do applied researchers use the causal forest? A methodological review. International Statistical Review, 93(2), 288-316. https://doi.org/10.1111/insr.12610
  13. Caetano, C., & Callaway, B. (2024). Difference-in-Differences when Parallel Trends Holds Conditional on Covariates. arXiv:2406.15288. https://arxiv.org/abs/2406.15288
  14. Callaway, B. (2024, September 10). Introduction to DiD with Multiple Time Periods (did vignette). https://bcallaway11.github.io/did/articles/multi-period-did.html
  15. Mogstad, M., & Torgovitsky, A. (2024). Instrumental Variables with Unobserved Heterogeneity in Treatment Effects. NBER Working Paper No. 32927. https://doi.org/10.3386/w32927
  16. Heath, A., Kunst, N., & Jackson, C. (Eds.). (2024). Value of Information for Healthcare Decision-Making. Taylor & Francis. https://doi.org/10.1201/9781003156109
  17. Center for Open Science. (2024). Transparency and Openness Promotion (TOP) Guidelines, Version 2.0. Charlottesville, VA. https://www.cos.io/initiatives/top-guidelines
  18. Thomke, S. (2020). Experimentation Works: The Surprising Power of Business Experiments. Harvard Business Review Press. https://doi.org/10.1080/08956308.2020.1762443
  19. Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. https://doi.org/10.1017/9781108653985

Full Text (.pdf)

Received: 23.09.2025
Accepted: 25.10.2025
Published: 29.12.2025