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).
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Full Text (.pdf)
Received: 23.09.2025
Accepted: 25.10.2025
Published: 29.12.2025