Centrality-oriented Causality -- A Study of EU Agricultural Subsidies and Digital Developement in Poland
Kosiorowski Daniel, Jerzy P. Rydlewski

TL;DR
This paper introduces a centrality-oriented causality framework using data depth concepts to improve causal inference in socio-economic studies, demonstrated through EU subsidies' impact on Poland's digital development.
Contribution
It proposes a novel modification of Rubin's causality concept incorporating data depth, enabling causal analysis with existing statistical databases in socio-economic research.
Findings
Data depth methods effectively identify causal effects in socio-economic data.
EU agricultural subsidies are shown to influence digital development in Poland.
The framework accommodates challenges of real-world socio-economic data.
Abstract
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…
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