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  • 1.
    Kakooei, Mohammad
    et al.
    Linköpings universitet. Chalmers Univ Technol, Sweden.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Chalmers Univ Technol, Sweden; Stanford Univ, CA 94305 USA.
    Increasing the Confidence of Predictive Uncertainty: Earth Observations and Deep Learning for Poverty Estimation2024Inngår i: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 62, artikkel-id 4704613Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Reducing global poverty, particularly in low- and middle-income countries, is a critical objective of the sustainable development goals (SDGs). To track progress toward these goals, high-frequency, granular geo-temporal data that capture changes at the neighborhood level is essential for researchers and policymakers. Recent advancements in methodology have combined machine learning (ML) and Earth observations (EOs) for poverty estimation, thereby addressing significant data gaps. However, a notable limitation of these EO-ML methods is their frequent deployment without a robust mechanism to quantify predictive uncertainty. Understanding this uncertainty is crucial for making informed decisions, effectively managing risks, and instilling confidence in users and stakeholders regarding the model's predictions. Although deep learning (DL) offers methods to quantify predictive uncertainties, their reliability is often constrained, failing to accurately reflect the underlying variations in predictions. Our proposed method aims to enhance confidence in predictive uncertainty without sacrificing accuracy. It begins by integrating an external model to explicitly capture data variability. Subsequently, we employ two orthogonal metrics-accuracy and uncertainty-to evaluate the influence of training data, especially in scenarios involving satellite imagery (e.g., selecting a subset of source domain countries for prediction in the target country). By applying these metrics, we formulate criteria to assess the importance of choosing specific countries from the source domain as training data. Our analysis highlights the effectiveness of this methodology in situations where the target country offers high-dimensional data, like satellite images, but faces a shortage of adequate training samples for DL models.

  • 2.
    Daoud, Adel
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Harvard Univ, MA USA; Chalmers Univ Technol, Sweden.
    Johansson, Fredrik D.
    Chalmers Univ Technol, Sweden.
    The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low-and middle-income countries2024Inngår i: Social Science Research, ISSN 0049-089X, E-ISSN 1096-0317, Vol. 118, artikkel-id 102973Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synthesizes theories from family sociology and political science to examine the heterogeneous effects on child poverty of economic shocks following the implementation of an International Monetary Fund (IMF) program. To discover effect heterogeneity, we apply machine learning to policy evaluation. We find that children's average probability of falling into poverty increases by 14 percentage points. We find substantial effect heterogeneity, with family wealth and governments' education spending as the two most important moderators. In contrast to studies that emphasize the vulnerability of low-income families, we find that middle-class children face an equally high risk of poverty. Our results show that synthesizing family and political factors yield deeper knowledge of how economic shocks affect children.

  • 3.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Stanford Univ, CA USA; Dept Comp Sci & Engn, Sweden.
    A theory of famines-A response2023Inngår i: Journal of International Development, ISSN 0954-1748, E-ISSN 1099-1328Artikkel i tidsskrift (Annet vitenskapelig)
  • 4.
    Daoud, Adel
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Chalmers Univ Technol, Sweden.
    Jordan, Felipe
    Pontificia Univ Catolica Chile, Chile; Pontificia Univ Catolica Chile, Chile.
    Sharma, Makkunda
    Wadhwani AI, India; Indian Inst Technol Delhi, India.
    Johansson, Fredrik
    Chalmers Univ Technol, Sweden.
    Dubhashi, Devdatt
    Chalmers Univ Technol, Sweden.
    Paul, Sourabh
    Indian Inst Technol Delhi, India.
    Banerjee, Subhashis
    Ashoka Univ, India; Indian Inst Technol Delhi, India.
    Using Satellite Images and Deep Learning to Measure Health and Living Standards in India2023Inngår i: Social Indicators Research, ISSN 0303-8300, E-ISSN 1573-0921, Vol. 167, s. 475-505Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Using deep learning with satellite images enhances our understanding of human development at a granular spatial and temporal level. Most studies have focused on Africa and on a narrow set of asset-based indicators. This article leverages georeferenced village-level census data from across 40% of the population of India to train deep models that predicts 16 indicators of human well-being from Landsat 7 imagery. Based on the principles of transfer learning, the census-based model is used as a feature extractor to train another model that predicts an even larger set of developmental variables-over 90 variables-included in two rounds of the National Family Health Survey (NFHS). The census-based-feature-extractor model outperforms the current standard in the literature for most of these NFHS variables. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep models that track human development at an unprecedented geographical and temporal resolution.

    Fulltekst (pdf)
    fulltext
  • 5.
    Daoud, Adel
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Harvard Univ, MA 02138 USA; Chalmers Univ Technol, Sweden.
    Jerzak, Connor
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Harvard Univ, MA 02138 USA.
    Johansson, Richard
    Univ Gothenburg, Sweden; Chalmers Univ Technol, Sweden.
    Conceptualizing Treatment Leakage in Text-based Causal Inference2022Inngår i: NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES, ASSOC COMPUTATIONAL LINGUISTICS-ACL , 2022, s. 5638-5645Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Causal inference methods that control for text-based confounders are becoming increasingly important in the social sciences and other disciplines where text is readily available. However, these methods rely on a critical assumption that there is no treatment leakage: that is, the text only contains information about the confounder and no information about treatment assignment. When this assumption does not hold, methods that control for text to adjust for confounders face the problem of posttreatment (collider) bias. However, the assumption that there is no treatment leakage may be unrealistic in real-world situations involving text, as human language is rich and flexible. Language appearing in a public policy document or health records may refer to the future and the past simultaneously, and thereby reveal information about the treatment assignment. In this article, we define the treatment-leakage problem, and discuss the identification as well as the estimation challenges it raises. Second, we delineate the conditions under which leakage can be addressed by removing the treatment-related signal from the text in a preprocessing step we define as text distillation. Lastly, using simulation, we show how treatment leakage introduces a bias in estimates of the average treatment effect (ATE) and how text distillation can mitigate this bias.

  • 6.
    Daoud, Adel
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Chalmers Univ Technol, Sweden; Harvard Univ, MA 02138 USA.
    Herlitz, Anders
    Harvard Univ, MA 02115 USA; Harvard Univ, MA 02115 USA.
    Subramanian, S. V.
    Harvard Univ, MA 02138 USA; Inst Futures Studies, Sweden.
    IMF fairness: Calibrating the policies of the International Monetary Fund based on distributive justice2022Inngår i: World Development, ISSN 0305-750X, E-ISSN 1873-5991, Vol. 157, artikkel-id 105924Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The International Monetary Fund (IMF) provides financial assistance to its member countries in economic difficulties but at the same time requires these countries to reform public policies. In several contexts, these reforms have been at odds with population health and material living standards. While researchers have empirically analyzed the consequences of IMF reforms on health, no analysis has yet identified under what conditions tradeoffs between consequences for populations and economic outcomes would be fair and acceptable. Our article analyzes and identifies five principles to govern such tradeoffs and thus define IMF fairness. The article first reviews existing policy-evaluation studies, which on balance show that IMF policies, in their pursuit of macroeconomic improvement, frequently produce adverse effects on childrens health and material living standards. Secondly, the article discusses four theories from distributive ethics-maximization, egalitarianism, prioritarianism, and sufficientarianism-to identify which is most compatible with the IMFs core mission of improving macroeconomic conditions, while at the same time balancing the consequences for population outcomes. Using a distributive justice analysis of IMF policies, we argue that sufficientarianism constitutes the most compatible theory. Thirdly, the article formalizes IMF fairness in the language of causal inference. It also supplies a framework for empirically measuring the extent to which IMF policies fulfill the criteria of IMF fairness, using observational data.

  • 7.
    Shiba, Koichiro
    et al.
    Harvard TH Chan Sch Publ Hlth, MA USA; Harvard TH Chan Sch Publ Hlth, MA USA; Harvard Univ, MA USA; 677 Huntington Ave, MA 02115 USA.
    Hikichi, Hiroyuki
    Kitasato Univ, Japan.
    Okuzono, Sakurako S.
    Harvard TH Chan Sch Publ Hlth, MA USA.
    VanderWeele, Tyler J.
    Harvard TH Chan Sch Publ Hlth, MA USA; Harvard Univ, MA USA; Harvard TH Chan Sch Publ Hlth, MA USA.
    Arcaya, Mariana
    MIT, MA USA.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Chalmers Univ Technol, Sweden.
    Cowden, Richard G.
    Harvard Univ, MA USA.
    Yazawa, Aki
    Harvard TH Chan Sch Publ Hlth, MA USA; Natl Ctr Global Hlth & Med, Japan.
    Zhu, David T.
    Western Univ, Canada.
    Aida, Jun
    Tokyo Med & Dent Univ, Japan.
    Kondo, Katsunori
    Natl Ctr Geriatr & Gerontol, Japan; Chiba Univ, Japan.
    Kawachi, Ichiro
    Harvard TH Chan Sch Publ Hlth, MA USA.
    Long-Term Associations between Disaster-Related Home Loss and Health and Well-Being of Older Survivors: Nine Years after the 2011 Great East Japan Earthquake and Tsunami2022Inngår i: Journal of Environmental Health Perspectives, ISSN 0091-6765, E-ISSN 1552-9924, Vol. 130, nr 7, artikkel-id 077001Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND: Little research has examined associations between disaster-related home loss and multiple domains of health and well-being, with extended long-term follow-up and comprehensive adjustment for pre-disaster characteristics of survivors. OBJECTIVES: We examined the longitudinal associations between disaster-induced home loss and 34 indicators of health and well-being, assessed similar to 9 y post-disaster. METHODS: We used data from a preexisting cohort study of Japanese older adults in an area directly impacted by the 2011 Japan Earthquake (n = 3,350 and n = 2,028, depending on the outcomes). The study was initiated in 2010, and disaster-related home loss status was measured in 2013 retrospectively. The 34 outcomes were assessed in 2020 and covered dimensions of physical health, mental health, health behaviors/sleep, social well-being, cognitive social capital, subjective well-being, and prosocial/altruistic behaviors. We estimated the associations between disaster-related home loss and the outcomes, using targeted maximum likelihood estimation and SuperLearner. We adjusted for pre-disaster characteristics from the wave conducted 7 months before the disaster (i.e., 2010), including prior outcome values that were available. RESULTS: After Bonferroni correction for multiple testing, we found that home loss (vs. no home loss) was associated with increased posttraumatic stress symptoms (standardized difference = 0.50; 95% CI: 0.35, 0.65), increased daily sleepiness (0.38; 95% CI: 0.21, 0.54), lower trust in the community (-0.36; 95% CI: -0.53, -0.18), lower community attachment (-0.60; 95% CI: -0.75, -0.45), and lower prosociality (-0.39; 95% CI: -0.55, -0.24). We found modest evidence for the associations with increased depressive symptoms, increased hopelessness, more chronic conditions, higher body mass index, lower perceived mutual help in the community, and decreased happiness. There was little evidence for associations with the remaining 23 outcomes. DISCUSSION: Home loss due to a disaster may have long-lasting adverse impacts on the cognitive social capital, mental health, and prosociality of older adult survivors.

    Fulltekst (pdf)
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  • 8.
    Balgi, Sourabh
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Peña, Jose M.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows2022Inngår i: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence: AAAI Special Track on AI for Social Impact, Palo Alto, California USA: AAAI Press, 2022, Vol. 36, nr 11, s. 11810-11818, artikkel-id 21437Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P3A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P3A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P3A. A major advantage of c-GNF is that it suits the open system in which P3A is conducted. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. This capturing capability is enabled by the deep neural networks that model the underlying SCM via observational data likelihood maximization using gradient descent. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ACE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitating P3A, optimizing social interventions - in contrast to the current `one-size-fits-all' approach of existing methods.

  • 9.
    Shiba, Koichiro
    et al.
    Harvard TH Chan Sch Publ Hlth, MA 02115 USA; Harvard TH Chan Sch Publ Hlth, MA 02115 USA.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Harvard TH Chan Sch Publ Hlth, MA USA; Chalmers Univ Technol, Sweden.
    Kino, Shiho
    Univ Tokyo, Japan; Kyoto Univ, Japan.
    Nishi, Daisuke
    Univ Tokyo, Japan.
    Kondo, Katsunori
    Chiba Univ, Japan; Natl Ctr Geriatr & Gerontol, Japan.
    Kawachi, Ichiro
    Harvard TH Chan Sch Publ Hlth, MA 02115 USA.
    Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach2022Inngår i: Psychiatry and Clinical Neurosciences, ISSN 1323-1316, E-ISSN 1440-1819, Vol. 76, nr 4, s. 97-105Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aim Understanding the differential mental health effects of traumatic experiences is important to identify particularly vulnerable subpopulations. We examined the heterogeneous associations between disaster-related traumatic experiences and postdisaster mental health, using a novel machine learning-based causal inference approach. Methods Data were from a prospective cohort study of Japanese older adults in an area severely affected by the 2011 Great East Japan Earthquake. The baseline survey was conducted 7 months before the disaster and the 2 follow-up surveys were conducted 2.5 and 5.5 years after (n = 1150 to n = 1644 depending on the exposure-outcome combinations). As disaster-related traumatic experiences, we assessed complete home loss and loss of loved ones. Using the generalized random forest algorithm, we estimated conditional average treatment effects (CATEs) of the disaster damages on postdisaster mental health outcomes to examine the heterogeneous associations by 51 predisaster characteristics of the individuals. Results We found that, even when there was no population average association between disaster-related trauma and subsequent mental health outcomes, some subgroups experienced severe impacts. We also identified and compared characteristics of the most and least vulnerable groups (ie, top versus bottom deciles of the estimated CATEs). While there were some unique patterns specific to each exposure-outcome combination, the most vulnerable group tended to be from lower socioeconomic backgrounds with preexisting depressive symptoms for many exposure-outcome combinations. Conclusions We found considerable heterogeneity in the association between disaster-related traumatic experiences and subsequent mental health problems.

  • 10.
    Kino, Shiho
    et al.
    Harvard TH Chan Sch Publ Hlth, MA USA; Kyoto Univ, Japan.
    Hsu, Yu-Tien
    Harvard TH Chan Sch Publ Hlth, MA USA.
    Shiba, Koichiro
    Harvard TH Chan Sch Publ Hlth, MA USA.
    Chien, Yung-Shin
    Harvard TH Chan Sch Publ Hlth, MA USA.
    Mita, Carol
    Harvard Univ, MA 02115 USA.
    Kawachi, Ichiro
    Harvard TH Chan Sch Publ Hlth, MA USA.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Harvard Univ, MA 02115 USA; Univ Gothenburg, Sweden; Chalmers Univ Technol, Sweden.
    A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects2021Inngår i: SSM - Population Health, ISSN 2352-8273, Vol. 15, artikkel-id 100836Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Background: Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods: Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results: Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions: While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

    Fulltekst (pdf)
    fulltext
  • 11.
    Shiba, Koichiro
    et al.
    Harvard TH Chan Sch Publ Hlth, MA 02115 USA; Harvard TH Chan Sch Publ Hlth, MA 02115 USA.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten. Harvard TH Chan Sch Publ Hlth, MA USA; Chalmers Univ Technol, Sweden.
    Hikichi, Hiroyuki
    Kitasato Univ, Japan.
    Yazawa, Aki
    Harvard TH Chan Sch Publ Hlth, MA 02115 USA.
    Aida, Jun
    Tokyo Med & Dent Univ, Japan.
    Kondo, Katsunori
    Chiba Univ, Japan; Natl Ctr Geriatr & Gerontol, Japan.
    Kawachi, Ichiro
    Harvard TH Chan Sch Publ Hlth, MA 02115 USA.
    Heterogeneity in cognitive disability after a major disaster: A natural experiment study2021Inngår i: Science Advances, E-ISSN 2375-2548, Vol. 7, nr 40, artikkel-id eabj2610Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cognitive disability following traumatic experiences of disaster has been documented; however, little is known about heterogeneity in the association across individuals. In this natural experiment study of approximately 3000 Japanese older adults in an area directly affected by the 2011 Great East Japan Earthquake, the baseline survey was established 7 months before the 2011 earthquake. To inductively identify heterogeneity in post-disaster cognitive disability by predisaster characteristics, we applied a machine learning-based causal inference approach-generalized random forest. We identified strong evidence for heterogeneity in the association between home loss and cognitive disability objectively assessed 2.5 and 5.5 years after the 2011 earthquake. The subgroups with the strongest disaster-dementia associations tended to be from low socioeconomic backgrounds and have predisaster health problems. The study demonstrated that some subpopulations are particularly prone to experience cognitive disability after disasters, which could be overlooked in studies assessing population average associations only.

    Fulltekst (pdf)
    fulltext
  • 12.
    Balgi, Sourabh
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Peña, Jose M.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten.
    Counterfactual Analysis of the Impact of the IMF Program on Child Povertyin the Global-South Region using Causal-Graphical Normalizing FlowsManuskript (preprint) (Annet vitenskapelig)
    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'}.

  • 13.
    Balgi, Sourabh
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten.
    Peña, Jose M.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Wodtke, Geoffrey
    Department of Sociology, University of Chicago, Chicago, IL, USA.
    Zhou, Jesse
    Department of Sociology, University of Chicago, Chicago, IL, USA.
    Deep Learning With DAGsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in practice. As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify the task of empirical evaluation, researchers tend to invoke such assumptions anyway, even though they are typically arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the complexity of the causal system under investigation. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional approaches, cGNFs model the full joint distribution of the data according to a DAG supplied by the analyst, without relying on stringent assumptions about functional form. In this way, the method allows for flexible, semi-parametric estimation of any causal estimand that can be identified from the DAG, including total effects, conditional effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s (1967) model of status attainment and Zhou’s (2019) model of conditional versus controlled mobility. To facilitate adoption, we provide open-source software together with a series of online tutorials for implementing cGNFs. The article concludes with a discussion of current limitations and directions for future development.

  • 14.
    Balgi, Sourabh
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Peña, Jose M.
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Daoud, Adel
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten.
    ρ-GNF: A Novel Sensitivity Analysis Approach Under Unobserved ConfoundersManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    We propose a new sensitivity analysis model that combines copulas and normalizing flows for causal inference under unobserved confounding. We refer to the new model as ρ-GNF (ρ-Graphical Normalizing Flow), where ρ∈[−1,+1] is a bounded sensitivity parameter representing the backdoor non-causal association due to unobserved confounding modeled using the most well studied and widely popular Gaussian copula. Specifically, ρ-GNF enables us to estimate and analyse the frontdoor causal effect or average causal effect (ACE) as a function of ρ. We call this the ρcurve. The ρcurve enables us to specify the confounding strength required to nullify the ACE. We call this the ρvalue. Further, the ρcurve also enables us to provide bounds for the ACE given an interval of ρ values. We illustrate the benefits of ρ-GNF with experiments on simulated and real-world data in terms of our empirical ACE bounds being narrower than other popular ACE bounds.

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