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 treatment selection bias; (ii) a counterfactual generator to compute counterfactual outcomes for varying aid volumes to address small sample-size settings; and (iii) an inference model that is used to predict heterogeneous treatment-response curves. We demonstrate the effectiveness of our framework using data with official development aid earmarked to end HIV/AIDS in 105 countries, amounting to more than USD 5.2 billion. For this, we first show that our framework successfully computes heterogeneous treatment-response curves using semi-synthetic data. Then, we demonstrate our framework using real-world HIV data. Our framework points to large opportunities for a more effective aid allocation, suggesting that the total number of new HIV infections could be reduced by up to 3.3% (~50,000 cases) compared to the current allocation practice.

翻译：联合国可持续发展目标（SDGs）描绘了“不让任何人掉队”的更美好未来蓝图，而要在2030年前实现这些目标，贫困国家需要巨额发展援助。本文构建了一个因果机器学习框架，用于预测援助拨付的异质性处理效应，从而为有效的援助分配提供依据。具体而言，该框架由三个部分组成：(i) 平衡自编码器，利用表示学习嵌入高维国家特征，同时解决处理选择偏差；(ii) 反事实生成器，用于计算不同援助量下的反事实结果，以应对小样本场景；(iii) 推理模型，用于预测异质性处理-响应曲线。我们利用分配给105个国家用于终结HIV/AIDS的官方发展援助数据（总额超过52亿美元）验证了该框架的有效性。首先，我们通过半合成数据展示了该框架能成功计算异质性处理-响应曲线。随后，我们使用真实世界HIV数据验证了框架性能。实验结果表明，当前的援助分配方式存在巨大改进空间：相较于现有分配策略，新发HIV感染总数最多可降低3.3%（约5万例）。