Centrality-oriented causality : a study of EU agricultural subsidies and digital developement in Poland

• Autorzy: Kosiorowski Daniel , Rydlewski Jerzy P.

Identyfikatory

DOI

10.37190/ord200303

• Języki publikacji: EN

Abstrakty

EN

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 government 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 centrality-oriented causality concept. The presented framework is especially useful in the context of conducting causal inference based on official statistics, i.e., on the 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 digital development in Poland in the period 2012–2018.

Słowa kluczowe

EN

causal inference; counterfactuals; statistical depth function; robust statistical inference; digital development

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2020
• Tom: Vol. 30, No. 3
• Strony: 47--63
• Opis fizyczny: Bibliogr. 35 poz.,tab.

Twórcy

autor

Kosiorowski Daniel

• Department of Statistics, Cracow University of Economics, ul. Rakowicka 27, 31-510 Kraków, Poland

autor

Rydlewski Jerzy P.

• Faculty of Applied Mathematics, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Kraków, Poland

Bibliografia

• [1] BONGIORNO E.G., GOIA A., Classification methods for Hilbert data based on surrogate density, Comp. Stat. Data Anal., 2016, 99, 204–222.
• [2] BONGIORNO E.G., GOIA A., Describing the concentration of income populations by functional principal component analysis on Lorenz curves, J. Mult. Anal., 2019, 170, 10–24.
• [3] BOSQ D., Linear Processes in Function Spaces, Springer-Verlag, New York 2000.
• [4] COX D.R., WERMUTH N., Causality: a statistical view, Int. Stat. Rev., 2004, 72 (3), 285–305.
• [5] DAWID A.P., Causal inference without counterfactuals, J. Am. Stat. Assoc., 2000, 95 (450), 407–424.
• [6] DYCKERHOFF R., MOZHAROVSKYI P., Exact computation of the halfspace depth, Comp. Stat. Data Anal., 2016, 98, 19–30.
• [7] ENGLE R.F., WHITE S., Cointegration, causality, and forecasting. A Festschrift in Honour of Clive W.J. Granger, Oxford University Press, Oxford 1999.
• [8] FLEURBAEY M., MANIQUET F., A Theory of Fairness and Social Welfare, Cambridge University Press, Cambridge 2011.
• [9] FRAIMAN R., MUNIZ G., Trimmed means for functional data, Test, 2001, 10 (2), 419–440.
• [10] GIJBELS I., NAGY S., On a general definition of depth for functional data, Stat. Sci., 2017, 32 (4), 630–639.
• [11] GILL R.D., ROBINS J.M., Causal inference for complex longitudinal data. The continuous case, Ann. Stat., 2001, 29 (6), 1785–1811.
• [12] HENDRY D.F., Granger causality, Eur. J. Pure Appl. Math., 2017, 10 (1), 12–29.
• [13] JURECKOVA J., KALINA J., Nonparametric multivariate rank tests and their unbiasedness, Bernoulli, 2012, 18 (1), 229–251.
• [14] KLEINBERG S., Causality, Probability, and Time, Cambridge University Press, New York 2013.
• [15] KOSIOROWSKI D., ZAWADZKI Z., DepthProc: An R package for robust exploration of multidimensional economic phenomena, J. Stat. Soft. (forthcoming), 2020.
• [16] KOSIOROWSKI D., RYDLEWSKI J.P., ZAWADZKI Z., Functional outliers detection by the example of air quality monitoring, Stat. Rev., 2018, 65 (1), 81–98.
• [17] KOSIOROWSKI D., RYDLEWSKI J.P., SNARSKA M., Detecting a structural change in functional time series using local Wilcoxon statistic, Stat. Papers, 2019, 60, 1677–1698.
• [18] KOSIOROWSKI D., MIELCZAREK D.,RYDLEWSKI J.P., A critical study of usefulness of selected functional classifiers in economics, Acta Univ. Lodz.. Folia Oecon., 2020, 2 (347), 71–90.
• [19] LIU R.Y., SINGH K., A quality index based on data depth and multivariate rank tests, J. Am. Stat. Assoc., 1995, 88, 252–260.
• [20] LIU R.Y., PARELIUS J.M., SINGH K., Multivariate analysis by data depth. Descriptive statistics, graphics and inference (with discussion), Ann. Stat., 1999, 27 (3), 783–858.
• [21] LIU X., ZUO Y., Comppd: A Matlab package for computing projection depth, J. Stat. Soft., 2015, 65 (2), 1–21.
• [22] LIU X., ZUO Y., WANG Z., Exactly computing bivariate projection depth contours and median, Comp. Stat. Data Anal., 2013, 60, 1–11.
• [23] LOPEZ-PINTADO S., ROMO J., On the concept of depth for functional data, J. Am. Stat. Assoc., 2009, 104 (486), 718–734.
• [24] NAGY S., GIJBELS I., HLUBINKA D., Depth-based recognition of shape outlying functions, J. Comp. Graph. Stat., 2017, DOI: 10.1080/10618600.2017.1336445.
• [25] NARISETTY N.N., NAIR V.N., Extremal depth for functional data and applications, J. Am. Stat. Assoc., 2016, 111 (516), 1705–1714.
• [26] PAINDAVAINE D., VAN BEVER G., From depth to local depth: a focus on centrality, J. Am. Stat. Assoc., 2013, 105, 1105–1119.
• [27] PEARL J., Causality-Models, Reasoning, and Inference, Cambridge University Press, Cambridge 2000.
• [28] ROSENBAUM P.R., RUBIN D.B., The central role of the propensity score in observational studies for causal effects, Biometrika, 1983, 70 (1), 41–55.
• [29] ROUSSEEUW P.J., HUBERT M., Regression depth, J. Am. Stat. Assoc., 1999, 94 (446), 388–433.
• [30] RUBIN D.B., Estimating causal effects of treatment in randomized and nonrandomized studies, J. Edu. Psych., 1974, 66, 688–701.
• [31] RUBIN D.B., Causal inference using potential outcomes. Design, modeling, decisions, J. Am. Stat. Assoc., 2005, 100 (469), 322–331.
• [32] Statistics Poland, https://stat.gov.pl/, 2019, URL (access date: July 8, 2019).
• [33] WILCOX R., Introduction to Robust Estimation and Hypothesis Testing, Academic Press, 2014.
• [34] ZUO Y., Projection based depth functions and associated medians, Ann. Stat., 2003, 31 (5), 1460–1490.
• [35] ZUO Y., SERFLING R., General notions of statistical depth function, Ann. Stat., 2000, 28, 461–482.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).