Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects
Dec 30, 2017
Jianing Zhao, Daniel M. Runfola, Peter Kemper
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Zhao J., Runfola D.M., Kemper P. (2017) Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects. In: Altun Y. et al. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, vol 10536. Springer, Cham.
The World Bank provides billions of dollars in development finance to countries across the world every year. As many projects are related to the environment, we want to understand the World Bank projects impact to forest cover. However, the global extent of these projects results in substantial heterogeneity in impacts due to geographic, cultural, and other factors. Recent research by Athey and Imbens has illustrated the potential for hybrid machine learning and causal inferential techniques which may be able to capture such heterogeneity. We apply their approach using a geolocated dataset of World Bank projects, and augment this data with satellite-retrieved characteristics of their geographic context (including temperature, precipitation, slope, distance to urban areas, and many others). We use this information in conjunction with causal tree (CT) and causal forest (CF) approaches to contrast 'control' and 'treatment' geographic locations to estimate the impact of World Bank projects on vegetative cover.
World Bank IBRD-IDA Projects
World Bank Geocoded Research Release, Version 1.4.2
This geocoded dataset includes all projects approved from 1995-2014 in the World Bank IBRD/IDA lending lines. It tracks more than $630 billion in commitments for 5,684 projects across 61,243 locations.