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Estimation of causality in economic growth and expansionary policies using uplift modeling

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Abstract

Uplift modeling corresponds to an area of machine learning focused on capturing causal relationships on various observational and experimental data. Currently it has several applications, particularly in the marketing area, focused on customer segmentation and the establishment of advertising campaigns. This research proposes an novel economic uplift approach, using branching causal algorithms to estimate the individual treatment effect on real GDP growth and changes in expansionary economic policy on a quarterly basis from an OECD dataset. The developed framework reveals positive causal effects on economic growth driven by expansionary policies, generalized for all countries under study. In addition, lagged causal effects on these policies, exerted by the economic cycle, are captured. The results obtained not only show a performance similar to that of the literature, but also conform to the theoretical and actual macroeconomic behavior.

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Data Availability

All the data were downloaded and are available from: (a) URL: https://databank.worldbank.org. (b) URL: https://data.oecd.org/.

References

  1. Amaral A, Dyhoum T, Abdou H, Aljohani H (2022) Modeling for the relationship between monetary policy and GDP in the USA using statistical methods. Mathematics 10:4137. https://doi.org/10.3390/math10214137

    Article  Google Scholar 

  2. Amiri A, Gerdtham U (2012) Granger causality between exports, imports and GDP in France: evidence from using geostatistical models. Econ Res Guard 2:43–59

    Google Scholar 

  3. Athey S, Imbens G (2019) Machine learning methods that economists should know about. Ann Rev Econ 11:685–725

    Article  Google Scholar 

  4. Avouyi-Dovi S, Matheron J (2005) Interactions between business cycles, financial cycles and monetary policy: stylised facts. Invest Relat Financ Real Econ 22:273–98

    Google Scholar 

  5. Balk B, Reich U (2008) Additivity of national accounts reconsidered. J Econ Soc Meas 33:165–178

    Article  Google Scholar 

  6. Bantis E, Clements M, Urquhart A (2022) Forecasting GDP growth rates in the United States and Brazil using Google Trends. Int J Forecast (In Press)

  7. Beaudreau B (2010) On the methodology of energy-GDP Granger causality tests. Energy 35:3535–3539

    Article  Google Scholar 

  8. Becher M, Krol J (2020) Multiple treatments for uplift modeling. GitHub Repository. https://github.com/charlespwd/Matthias2193/APA

  9. Calderón C, Schmidt-Hebbel K (2008) Business cycles and fiscal policies: the role of institutions and financial markets (Central Bank of Chile). https://ideas.repec.org/p/chb/bcchwp/481.html

  10. Dempster A (1990) Causality and statistics. J Stat Plan Inference 25:261–278

    Article  Google Scholar 

  11. Devriendt F, Moldovan D, Verbeke W (2018) A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: a step** stone toward the development of prescriptive analytics. Big Data 6:13–41

    Article  Google Scholar 

  12. Dietterich TG (2000) Ensemble methods in machine learning. In: Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings 1. Springer Berlin Heidelberg, pp 1–15

  13. Granger C (1988) Causality, cointegration, and control. J Econ Dyn Control 12:551–559

    Article  Google Scholar 

  14. Gubela, Robin M, Lessmann, Stefan (2020) Uplift Forest for Multiple Treatments and Continuous Outcomes. Proceedings of International Conference on Information Systems (ICIS)

  15. Gubela RM, Lessmann S, Haupt J, Baumann A, Radmer T, Gebert F (2017) Revenue Uplift Modeling. Proceedings of the 38th International Conference on Information Systems (ICIS)

  16. Gutierrez P, Gérardy JY (2017) Causal inference and uplift modelling: A review of the literature. In: International conference on predictive applications and APIs. PMLR, pp 1–13

  17. Holland P (1986) Statistics and causal inference. J Am Stat Assoc 81:945–960

    Article  MathSciNet  MATH  Google Scholar 

  18. Hsing Y (2019) Is expansionary fiscal and monetary policy effective in Australia? Asian J Bus Environ 9:5–9

    Google Scholar 

  19. Jerić S, Zoričić D, Dolinar D (2020) Analysis of forecasts of GDP growth and inflation for the Croatian economy. Econ Res-Ekon Istraž 33:310–330

    Google Scholar 

  20. Lo V, Pachamanova D (2015) From predictive uplift modeling to prescriptive uplift analytics: a practical approach to treatment optimization while accounting for estimation risk. J Mark Anal 3:79–95

    Article  Google Scholar 

  21. Luetkepohl H, Xu F (2009) The role of the log transformation in forecasting economic variables. Emp Econ 42:619–638

  22. Lyeonov S, Pimonenko T, Bilan Y, Štreimikienė D, Mentel G (2019) Assessment of green investments’ impact on sustainable development: linking gross domestic product per capita, greenhouse gas emissions and renewable energy. Energies 12:3891

    Article  Google Scholar 

  23. Meng H, Qiao X (2022) Augmented direct learning for conditional average treatment effect estimation with double robustness. Electron J Stat 16:3523–3560

    Article  MathSciNet  MATH  Google Scholar 

  24. Nauta M, Bucur D, Seifert C (2019) Causal discovery with attention-based convolutional neural networks. Mach Learn Knowl Extr 1:312–340

    Article  Google Scholar 

  25. Olaya D, Vásquez J, Maldonado S, Miranda J, Verbeke W (2020) Uplift modeling for preventing student dropout in higher education. Decis Support Syst 134:113320

    Article  Google Scholar 

  26. Olaya D, Coussement K, Verbeke W (2020) A survey and benchmarking study of multitreatment uplift modeling. Data Min Knowl Discov 34, 273-308.

  27. Radcliffe N (2007) Using control groups to target on predicted lift: building and assessing uplift model. Dir Mark Anal J 1, 14–21

  28. Ranzato F, Zanella M (2020) Abstract interpretation of decision tree ensemble classifiers. Proc AAAI Confer Artif Intell 34:5478–5486

    Google Scholar 

  29. Rombaut E, Guerry M (2019) The effectiveness of employee retention through an uplift modeling approach. Int J Manpow 41(8):1199–1220

  30. Rosenbaum P, Rubin D (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55

    Article  MathSciNet  MATH  Google Scholar 

  31. Roush J, Siopes K, Hu G (2017) Predicting gross domestic product using autoregressive models. In: 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp 317–322

  32. Rubin D (1972) Estimating causal effects of treatments in experimental and observational studies. ETS Resd Bull Ser 1972:i–31

    Google Scholar 

  33. Rzepakowski P, Jaroszewicz S (2012) Uplift modeling in direct marketing. J Telecommun Inf Technol 2:43–50

  34. Shojaie A, Fox E (2022) Granger causality: a review and recent advances. Ann Rev Stat Appl 9:289–319

    Article  MathSciNet  Google Scholar 

  35. Vegetabile BG (2021) On the Distinction Between" Conditional Average Treatment Effects"(CATE) and" Individual Treatment Effects"(ITE) Under Ignorability Assumptions. ar**v preprint ar**v:2108.04939

  36. Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc 113:1228–1242

    Article  MathSciNet  MATH  Google Scholar 

  37. Yuan Y, Wu L, Zhang X (2021) Gini-impurity index analysis. IEEE Trans Inf Forens Secur 16:3154–3169

    Article  Google Scholar 

  38. Zhao Z, Harinen T (2019) Uplift Modeling for Multiple Treatments with Cost Optimization. 2019 IEEE International Conference On Data Science And Advanced Analytics (DSAA). pp. 422-431

  39. Zimmermann A (2008) Ensemble-trees: leveraging ensemble power inside decision trees

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Acknowledgements

This study was supported by ANID Fondecyt 1200555 fund.

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Authors and Affiliations

  1. Departamento de Industrias, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Cristhian Bermeo, Kevin Michell & Werner Kristjanpoller

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  1. Cristhian Bermeo
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  2. Kevin Michell
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  3. Werner Kristjanpoller
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Correspondence to Werner Kristjanpoller.

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Appendix

Appendix

See Appendix Tables 1, 2, 3, 4, 5.

Table 1 Quarterly GDP growth statistics of the 34 target countries for 1979 through 2021
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Table 2 Quarterly growth statistics of GDP, general government expenditure and long-term interest rates for 1979 through 2021
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Table 3 Number of observations for each economic policy for 1979 through 2021
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Table 4 Quarterly growth statistics of GDP, general government expenditure and long-term interest rates between economic policy data for 1979 through 2021
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Table 5 Quarterly growth statistics of GDP, general government expenditure and long-term interest rates between training and testing data
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Bermeo, C., Michell, K. & Kristjanpoller, W. Estimation of causality in economic growth and expansionary policies using uplift modeling. Neural Comput & Applic 35, 13631–13645 (2023). https://doi.org/10.1007/s00521-023-08397-0

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