Welcome to the Planetary Causal Inference Bulletin. Here, we share research updates, tutorials, and real-world case studies at the nexus of Earth observation and causal methods.
Latest post: Leveraging AI for Causal Inference in Planetary-Scale Data
Scaling Laws for Multi-scale Dynamics
Policymakers and researchers analyzing randomized controlled trials (RCTs) increasingly harness satellite imagery to capture the geographical, socioeconomic, or environmental covariates believed to moderate an intervention’s impact. Some of those moderating factors might be locally confined—such as...Continue reading
New Directions in Planetary Causal Inference
Planetary causal inference (PCI) is a field where we draw on experimental, observational, or natural experimental data from social, natural, and environmental sciences and link those sources to satellite imagery or other Earth Observation (EO) data....Continue reading
Planetary Causal Inference: Why Now?
Planetary causal inference is emerging as a field of inquiry focused on integration of planetary-scale Earth observation (EO) data with localized RCTs and natural experimental studies—why is it emerging now?....Continue reading
Earth Observation Meets Adam Smith
Satellite imagery is contributing to how we measure and understand global poverty. From miles above Earth, high-resolution photographs capture details of villages, farms, and cities that conventional surveys might have a hard time capturing. By analyzing these...Continue reading
Introducing the PCI RCT Repo
Randomized controlled trials (RCTs) long ago proved themselves as workhorses of rigorous social science, medical research, and policy evaluation. Yet, they can struggle to account for the spatial and contextual heterogeneity of real-world settings: there are so many factors....Continue reading
Platonic Representations for Poverty Mapping
We paired ~60k African DHS clusters with satellite imagery and two flavors of place‑specific text—LLM‑only spatiotemporal descriptions (“neural memory”) and web text fetched by an AI search agent—and asked whether these independent channels converge on the same latent signal of material well‑being. Fusing vision with LLM text explains about 77% of held‑out....Continue reading