  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.
