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Results of a convincing causal statistical inference related tosocio-economic phenomena are treated as especially desired background forconducting various socio-economic programs or government interventions.Unfortunately, quite often real socio-economic issues do not fulfillrestrictive assumptions of procedures of causal analysis proposed in theliterature. This paper indicates certain empirical challenges and conceptualopportunities related to applications of procedures of data depth concept intoa process of causal inference as to socio-economic phenomena. We show, how toapply a statistical functional depths in order to indicate factual andcounterfactual distributions commonly used within procedures of causalinference. Thus a modification of Rubin causality concept is proposed, i.e., acentrality-oriented causality concept. The presented framework is especiallyuseful in a context of conducting causal inference basing on officialstatistics, i.e., basing on already existing databases. Methodologicalconsiderations related to extremal depth, modified band depth, Fraiman-Munizdepth, and multivariate Wilcoxon sum rank statistic are illustrated by means ofexample related to a study of an impact of EU direct agricultural subsidies ona digital development in Poland in a period of 2012-2019.

Centrality-oriented Causality -- A Study of EU Agricultural Subsidies and Digital Developement in Poland