The AI & Global Development Lab uses AI and Earth‑observation data to map and analyse development at planetary scale. Here, we provide transcripts, gathering written versions of key talks from the Lab’s video library to make the content easier to review. Current transcripts include Adel Daoud’s lectures on confounding and on the overall concept of planetary causal inference, Kazuki Sakamoto’s scoping review of Earth‑observation and machine‑learning methods for causal inference, and Fucheng Warren Zhu’s talk on optimizing multi‑scale representations to detect effect heterogeneity.
- Isaac Corley presents a guest lecture: Cloud-Native GeoAI (@Gov 355M)
- Adel Daoud presents: A First Course in Planetary Causal Inference: Confounding (@IC2S2 2025)
- Adel Daoud presents: How AI and Satellites Reveal Poverty Traps (@Geo for Good)
- Adel Daoud presents: From Online Traces to Material Imprints: Articulating a Vision for Planetary Social Computing (@ICSC)
- Adel Daoud presents: Planetary Causal Inference: Overview (@Yale)
- Adel Daoud presents: Seminar on AI & Global Development (@Harvard Astrostatistics)
- Connor Jerzak presents: Seeing Like a Satellite While Learning Across Scales: Remote Audits + Multi-Scale Optimization for Heterogeneity (@Columbia)
- Satiyabooshan Murugaboopathy presents: Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty? (@ICSC)
- Markus Pettersson presents: Debiasing ML Predictions for Causal Inference Without Additional Ground Truth Data (@AAAI)
- 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 (@CLeaR)
See also [Video Library].