  Results of a convincing causal statistical inference related to
socio-economic phenomena are treated as especially desired background for
conducting various socio-economic programs or government interventions.
Unfortunately, quite often real socio-economic issues do not fulfill
restrictive assumptions of procedures of causal analysis proposed in the
literature. This paper indicates certain empirical challenges and conceptual
opportunities related to applications of procedures of data depth concept into
a process of causal inference as to socio-economic phenomena. We show, how to
apply a statistical functional depths in order to indicate factual and
counterfactual distributions commonly used within procedures of causal
inference. Thus a modification of Rubin causality concept is proposed, i.e., a
centrality-oriented causality concept. The presented framework is especially
useful in a context of conducting causal inference basing on official
statistics, i.e., basing on already existing databases. Methodological
considerations related to extremal depth, modified band depth, Fraiman-Muniz
depth, and multivariate Wilcoxon sum rank statistic are illustrated by means of
example related to a study of an impact of EU direct agricultural subsidies on
a digital development in Poland in a period of 2012-2019.
