  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'}.
