Transcripts

Planetary Causal Inference (PCI) is an emerging field that combines satellite imagery and other planetary‑scale data with localized studies, such as randomized controlled trials, to understand cause‑and‑effect relationships across multiple scales. This transcripts page provides text versions of talks given by members of the PCI community, including Adel Daoud’s sessions on confounding and on planetary causal inference, Kazuki Sakamoto’s review of Earth‑observation and machine‑learning approaches to causal inference, and Fucheng Warren Zhu’s presentation on optimizing multi‑scale representations. These transcripts complement the corresponding videos and allow readers to revisit key points in a textual format.

Isaac Corley presents a guest lecture: Cloud-Native GeoAI (@Gov 355M)
Adel Daoud presents: A First Course in Planetary Causal Inference: Confounding (@IC2S2)
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)
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)