Seminar 2024-10-23: Adel Daoud

Abstract Planetary Causal Inference explores how social science can benefit from Earth observation (EO) data to advance understanding of humans as a species and their impact on their environment, society, and economy. Traditional methods relying on tabular data, like surveys and national statistics, are costly and sometimes limited in scope, hindering planetary-scale analysis. EO data gathered via satellites offer a complementary approach that captures global, real-time information, enabling researchers to study phenomena like urbanization, poverty, conflict, and deforestation at fine spatial and temporal resolutions. We introduce the emerging field of causally-oriented EO-based machine learning (ML), where spatial data derived from images are analyzed using advanced ML models to create proxies for social science metrics and for use in causal inference pipelines. We discuss how these planetary causal inference methods can produce high-resolution insights about global social issues, providing new ways to assess conflict, sustainable development, and a range of other phenomena. By combining insights from geography, history, and multi-scale analysis, Planetary Causal Inference lays a foundation for researchers to address broad, integrated questions across household, neighborhood, regional, and global scales with a principled approach to inference.