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Causal Machine Learning for Cost-Effective Allocation of Development Aid. (arXiv:2401.16986v1 [stat.ML])
cs.LG updates on arXiv.org arxiv.org
The Sustainable Development Goals (SDGs) of the United Nations provide a
blueprint of a better future by 'leaving no one behind', and, to achieve the
SDGs by 2030, poor countries require immense volumes of development aid. In
this paper, we develop a causal machine learning framework for predicting
heterogeneous treatment effects of aid disbursements to inform effective aid
allocation. Specifically, our framework comprises three components: (i) a
balancing autoencoder that uses representation learning to embed
high-dimensional country characteristics while addressing …
arxiv cost development framework future machine machine learning paper stat.ml sustainable sustainable development united united nations