13 research outputs found

    Learning Large-Scale Bayesian Networks with the sparsebn Package

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    Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure

    Estimating networks of sustainable development goals

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    An increasing number of researchers and practitioners advocate for a systemic understanding of the Sustainable Development Goals (SDGs) through interdependency networks. Ironically, the burgeoning network-estimation literature seems neglected by this community. We provide an introduction to the most suitable estimation methods for SDG networks. Building a dataset with 87 development indicators in four countries over 20 years, we perform a comparative study of these methods. We find important differences in the estimated network structures as well as in synergies and trade-offs between SDGs. Finally, we provide some guidelines on the potentials and limitations of estimating SDG networks for policy advice

    Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

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    A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries in the data, SAM aims at recovering full causal models from continuous observational data along a multivariate non-parametric setting. The approach is based on a game between dd players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the overall joint conditional distribution, and that of the original data. An original learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the end-to-end optimization of the graph structure and parameters through stochastic gradient descent. Besides the theoretical analysis of the approach in the large sample limit, SAM is extensively experimentally validated on synthetic and real data

    How does government expenditure impact sustainable development? Studying the multidimensional link between budgets and development gaps

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    Abstract We develop a bottom-up causal framework to study the impact of public spending on high-dimensional and interdependent policy spaces in the context of socioeconomic and environmental development. Using data across 140 countries, we estimate the indicator-country-specific development gaps that will remain open in 2030. We find large heterogeneity in development gaps, and non-linear responses to changes in the total amount of government expenditure. Importantly, our method identifies bounds to how much a gap can be reduced by 2030 through sheer increments in public spending. We show that these structural bottlenecks cannot be addressed through expenditure on the existing government programs, but require novel micro-policies intended to affect behaviors, technologies, and organizational practices. One particular set of bottlenecks that stands out relates to the environmental issues contained in the sustainable development goals 14 and 15

    Subnational sustainable development: The role of vertical intergovernmental transfers in reaching multidimensional goals

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    Achieving sustainable development hinges on two critical factors: the subnational implementation of public policies and the efficient allocation of resources across regions through vertical intergovernmental transfers. We introduce a framework that links these two mechanisms for analyzing the impact of reallocating federal transfers in the presence of regional heterogeneity from development indicators, budget sizes, expenditure returns, and long-term structural factors. Our study focuses on the case of Mexico and its 32 states. Using an agent-based computational model, we estimate the development gaps that will remain by the year 2030, and characterize their sensitivity to changes in the states’ budget sizes. Then, we estimate the optimal distribution of federal transfers to minimize these gaps. Crucially, these distributions depend on the specific development objectives set by the national government, and by various interdependencies between the heterogeneous qualities of the states. This work sheds new light on the complex problem of budgeting for the Sustainable Development Goals at the subnational level, and it is especially relevant for the study of fiscal decentralization from the expenditure point of view

    Policy priority inference: A computational framework to analyze the allocation of resources for the sustainable development goals

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    We build a computational framework to support the planning of development and the evaluation of budgetary strategies toward the 2030 Agenda. The methodology takes into account some of the complexities of the political economy underpinning the policymaking process: the multidimensionality of development, the interlinkages between these dimensions, and the inefficiencies of policy interventions, as well as institutional factors that promote or discourage these inefficiencies. The framework is scalable and usable even with limited publicly available information: development-indicator data. However, it can be further refined as more data becomes available, for example, on public expenditure. We demonstrate its usage through an application for the Mexican federal government. For this, we infer historical policy priorities, that is, the non-observable allocations of transformative resources that generated past changes in development indicators. We also show how to use the tool to assess the feasibility of development goals, to measure policy coherence, and to identify accelerators. Overall, the framework and its computational tools allow policymakers and other stakeholders to embrace a complexity (and a quantitative) view to tackle the challenges of the Sustainable Development Goals

    Does expenditure in public governance guarantee less corruption? Non-linearities and complementarities of the rule of law

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    Corruption is an endemic societal problem with profound implications in the development of nations. In combating this issue, cross-national evidence supporting the effectiveness of the rule of law seems at odds with poorly realized outcomes from reforms inspired in the academic literature. This paper provides an explanation for such contradiction. By building a computational approach, we develop three methodological novelties into the empirical study of corruption: (1) modeling government expenditure as a more adequate intervention variable than traditional indicators, (2) generating large within-country variation by means of bottom-up simulations (instead of cross-national data pooling), and (2) accounting for all possible interactions between covariates through a spillover network. Our estimates suggest that, the least developed a country is, the more difficult it is to find the right combination of policies that lead to reductions in corruption. We characterize this difficulty through a rugged landscape that governments navigate when changing the total budget size and the relative expenditure towards the rule of law. Importantly our method helps identifying the—country-specific—policy issues that complement the rule of law in the fight against corruption
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