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Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows

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  • Sourabh Balgi
  • Jose M. Pe~na
  • Adel Daoud

Abstract

This work demonstrates the application of a particular branch of causal inference and deep learning models: \emph{causal-Graphical Normalizing Flows (c-GNFs)}. In a recent contribution, scholars showed that normalizing flows carry certain properties, making them particularly suitable for causal and counterfactual analysis. However, c-GNFs have only been tested in a simulated data setting and no contribution to date have evaluated the application of c-GNFs on large-scale real-world data. Focusing on the \emph{AI for social good}, our study provides a counterfactual analysis of the impact of the International Monetary Fund (IMF) program on child poverty using c-GNFs. The analysis relies on a large-scale real-world observational data: 1,941,734 children under the age of 18, cared for by 567,344 families residing in the 67 countries from the Global-South. While the primary objective of the IMF is to support governments in achieving economic stability, our results find that an IMF program reduces child poverty as a positive side-effect by about 1.2$\pm$0.24 degree (`0' equals no poverty and `7' is maximum poverty). Thus, our article shows how c-GNFs further the use of deep learning and causal inference in AI for social good. It shows how learning algorithms can be used for addressing the untapped potential for a significant social impact through counterfactual inference at population level (ACE), sub-population level (CACE), and individual level (ICE). In contrast to most works that model ACE or CACE but not ICE, c-GNFs enable personalization using \emph{`The First Law of Causal Inference'}.

Suggested Citation

  • Sourabh Balgi & Jose M. Pe~na & Adel Daoud, 2022. "Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows," Papers 2202.09391, arXiv.org.
  • Handle: RePEc:arx:papers:2202.09391
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    References listed on IDEAS

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    1. Daoud, Adel, 2018. "Unifying Studies of Scarcity, Abundance, and Sufficiency," Ecological Economics, Elsevier, vol. 147(C), pages 208-217.
    2. Geoffrey T. Wodtke, 2020. "Regression-based Adjustment for Time-varying Confounders," Sociological Methods & Research, , vol. 49(4), pages 906-946, November.
    3. Halleröd, Björn & Rothstein, Bo & Daoud, Adel & Nandy, Shailen, 2013. "Bad Governance and Poor Children: A Comparative Analysis of Government Efficiency and Severe Child Deprivation in 68 Low- and Middle-income Countries," World Development, Elsevier, vol. 48(C), pages 19-31.
    4. Daoud, Adel & Johansson, Fredrik, 2019. "Estimating Treatment Heterogeneity of International Monetary Fund Programs on Child Poverty with Generalized Random Forest," SocArXiv awfjt, Center for Open Science.
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    Cited by:

    1. Daoud, Adel & Herlitz, Anders & Subramanian, S.V., 2022. "IMF fairness: Calibrating the policies of the International Monetary Fund based on distributive justice," World Development, Elsevier, vol. 157(C).
    2. Sourabh Balgi & Jose M. Pe~na & Adel Daoud, 2022. "$\rho$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows," Papers 2209.07111, arXiv.org, revised Aug 2024.

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