Sourabh Balgi, Jose M. Peña, Adel Daoud
arXiv 17 Feb 2022 · Artificial Intelligence · 3 citations (OpenAlex)
arXiv:2202.09391 · PDF · DOI · OpenAlex · Extracted main text
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$\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 `The First Law of Causal Inference'.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pearl, J (2009) Causality: Models, Reasoning and Inference | 1.000 | 8 | 4 | 100% |
| 2 | Daoud, A. and Johansson, F (2019) Estimating treatment heterogeneity of international monetary fund programs on child poverty with generalized random forest self | 1.000 | 7 | 3 | 100% |
| 3 | Daoud, A., Nosrati, E., Reinsberg, B., Kentikelenis, A. E., Stubbs,… (2017) Impact of international monetary fund programs on child health self | 1.000 | 5 | 3 | 100% |
| 4 | Pearl, J (2009) Causal inference in statistics: An overview | 1.000 | 5 | 3 | 100% |
| 5 | Banerjee, A., Banerjee, A. V., and Duflo, E (2011) Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty | 0.928 | 4 | 3 | 100% |
| 6 | Pearl, J. and Mackenzie, D (2018) The Book of Why: The New Science of Cause and Effect | 0.928 | 4 | 3 | 100% |
| 7 | Rosenbaum, P. R. and Rubin, D. B (1983) The central role of the propensity score in observational studies for causal effects | 0.737 | 3 | 2 | 100% |
| 8 | Balgi, S., Peña, J. M., and Daoud, A (2022) Personalized public policy analysis in social sciences using causal-graphical normalizing flows self | 0.737 | 3 | 2 | 100% |
| 9 | Daoud, A. and Reinsberg, B (2018) Structural adjustment, state capacity and child health: evidence from IMF programmes self | 0.737 | 3 | 2 | 100% |
| 10 | Halleröd, B., Rothstein, B., Daoud, A., and Nandy, S (2013) Bad governance and poor children: A comparative analysis of government efficiency and severe child deprivation in 68 low-and mid… self | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 48 scored citations.