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Counterfactual Analysis of the Impact of the IMF Program on Child Povertyin the Global-South Region using Causal-Graphical Normalizing Flows
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. (STIMA)ORCID iD: 0000-0002-3329-5533
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

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±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'}.

Keywords [en]
Child poverty, Counterfactuals, Global-South, Personalized Public Policy
National Category
Social Sciences Interdisciplinary
Identifiers
URN: urn:nbn:se:liu:diva-188950OAI: oai:DiVA.org:liu-188950DiVA, id: diva2:1700989
Funder
Swedish Research Council
Note

Preprint under submission

Available from: 2022-10-04 Created: 2022-10-04 Last updated: 2022-10-13Bibliographically approved

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Other links

https://arxiv.org/pdf/2202.09391.pdf

Authority records

Balgi, SourabhPeña, Jose M.Daoud, Adel

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Balgi, SourabhPeña, Jose M.Daoud, Adel
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The Division of Statistics and Machine LearningFaculty of Science & EngineeringThe Institute for Analytical Sociology, IASFaculty of Arts and Sciences
Social Sciences Interdisciplinary

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