The AI & Global Development Lab fuses modern machine‑learning with Earth‑observation data to illuminate the causes and consequences of human development across time and space — from neighbourhood‑level poverty mapping to the evaluation of large‑scale aid programmes. Our interdisciplinary team of computer scientists, economists, sociologists and political scientists works at the intersection of AI, earth observation and causal inference to generate open data, open models and open‑source software for the global research community.
- Adel Daoud presents: A First Course in Planetary Causal Inference: Confounding (@IC2S2 2025)
- Adel Daoud presents: Planetary Causal Inference: Overview (@Yale)
- Adel Daoud presents: Seminar on AI & Global Development (@Harvard Astrostatistics)
- Satiyabooshan Murugaboopathy presents: Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?
- Kazuki Sakamoto presents: A Scoping Review of Earth Observation and Machine Learning for Causal Inference
- Fucheng Warren Zhu presents: Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using EO and Computer Vision
| Category | Repos |
|---|---|
| Datasets | cjerzak/TemporalNeighborhoodMaterialWealthAfrica – neighorhood‑level International Wealth Index rasters (1984‑2022)– harmonised DHS asset indices. |
| Spaces | cjerzak/WealthAfrica_v1 – interactive map of predicted wealth trajectories. |
| Libraries | causalimages – R package for use in projects. |
- Pull requests are welcome on any public repo.
- For large contributions, open an issue first so we can align on scope.
- Major decisions are taken in biweekly lab meetings.
- Website: https://aidevlab.org
- GitHub: https://github.com/AIandGlobalDevelopmentLab
- General inquiries: info@aidevlab.org
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