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）通过"不让任何人掉队"的理念描绘了更美好未来的蓝图。为实现2030年SDGs目标，贫困国家需要巨额开发援助。本文提出一种因果机器学习框架，用于预测援助拨款的异质性处理效应，从而指导有效的援助分配。该框架包含三个核心组件：（i）平衡自编码器——通过表征学习嵌入高维国家特征，同时处理选择偏倚问题；（ii）反事实生成器——通过计算不同援助规模下的反事实结果，应对小样本数据场景；（iii）推断模型——用于预测异质性处理响应曲线。我们使用105个国家专门用于终结艾滋病病毒/艾滋病的官方发展援助数据（总额超52亿美元）验证了框架的有效性。首先通过半合成数据证明该框架能成功计算异质性处理响应曲线，随后利用真实世界艾滋病数据进行实证分析。研究表明，相较于现行分配方式，采用本框架可使新增艾滋病病毒感染总数最高减少3.3%（约5万例），这为提升援助分配效能提供了重要机遇。