Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects
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ML can play a role in causal inference, including in policy problems and in climate-relevant scenarios such as estimating the effects of temperature on human mortality and the effects of World Bank projects on vegetative cover.
“ML can play a role in causal inference [41,340,619], including in the context of policy problems [40,453] and in climate-relevant scenarios such as estimating the effects of temperature on human mortality [356] and the effects of World Bank projects on vegetative cover [883].”
From Tackling climate change with machine learning (2023) · cites this paper as [883]
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- Authors
- Zhao, J., Runfola, D. M., & Kemper, P.
- Affiliations
- William & Mary (US)
- Corresponding author
- Zhao, Jianing
- ORCID
- Runfola, Daniel M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 204-215
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2777482913
- Cited by
- 12
- References
- 15
- Citations by year
- 2025: 1 · 2022: 1 · 2021: 4 · 2020: 3 · 2018: 2 · 2017: 1
- FWCI
- 3.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
- Social Sciences › Development › International Development and Aid
- Keywords
- treatment effect heterogeneity, development finance, forest cover, environmental impact of development projects, causal trees, causal forest, geolocation data, geographic context
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