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Centrality-oriented causality. A study of EU agricultural subsidies and digital developement in Poland

100%

KOSIOROWSKI D. , RYDLEWSKI J. P.

tom 30

nr 3

47-63

Results of a convincing causal statistical inference related to socio-economic phenomena are treated as an especially desired background for conducting various socio-economic programs or gov-ernment interventions. Unfortunately, quite often real socio-economic issues do not fulfil 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 statistical functional depths 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 cen-trality-oriented causality concept. The presented framework is especially useful in the context of con-ducting causal inference based on official statistics, i.e., on the already existing databases. Methodo-logical 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 digital development in Poland in the period 2012–2018.

Challenges in causal inference from Personal Monitoring Devices

86%

Wiktorski T.

2018

tom Vol. 16

99--102

Personal Monitoring Devices (PMDs) collect im- mense amount of data about health and wellness of hundreds of millions of people. One of the obstacles of the prevailing data analytics approaches to PMDs' data is limited value of correlation-based conclusions in a health context. Causal inference seems a natural solution, but general causal inference methodologies are difficult to apply to PMDs data due to size and complexity of observational data. Some methods, such as randomized trials, are largely infeasible in PMDs' context due to lack of control over the investigated population. In this paper, we overview existing approaches to causal inference including recent works that attempt to take advantage of time series data to automatically derive causality using extended difference- in-deference or Granger methods. We then outline challenges and opportunities for causal inference in the health context. Finally, we propose a following challenge: can we establish a new standard of evidence and a study design process that: (1) allows for drawing causal conclusions from large observational datasets and (2) can suggest interventions to enforce causal links discovered in the data.