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

Abstract

This work demonstrates the application of a particular branch of causal inference and deep learning models: 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 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 'The First Law of Causal Inference'.

Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows

“…We can still do better in terms of variance by appropriate selection of the deep neural networks as a part of the hyperparameter tuning, which is not our aim. A real-world application of c-GNF on a large-scale non-randomized observational study to analyse the impact of IMF (International Monetary Fund) program on the child poverty is conducted in Balgi, Peña, and Daoud (2022) that observes an effec-tive reduction of child poverty by 1.2±0.24 degrees in the Global-South, thus indicating the beneficial nature of IMF program on child poverty reduction.…”

Statistical Modeling: The Three Cultures

“…For example, in our famine case, this probability can refer to a specific Bengali farmer: would the farmer have survived had the Bengali government distributed food coupons (entitlements to food) to farmers , given that this farmer actually starved to death and did not receive coupons (Daoud, 2017). Although counterfactuals necessarily rely on stronger assumptions than calculating average effects, they present an exciting path for applied domains, such as personalized medicine (Gottesman et al, 2019), precision agriculture (Bauer et al, 2019), and public policy (Balgi, Peña, & Daoud, 2022a, 2022b. Pearl takes this statement one step further by arguing that "Advances in graphical and structural models have made counterfactuals computationally manageable and thus rendered causal reasoning a viable component in support of strong AI" (Pearl, 2019, p. 1).…”