Data, models, and methods for planetary-scale causal inference, text-based AI systems, and other interests. Led by Connor T. Jerzak, Assistant Professor at UT Austin.
Planetary Causal Inference
Harnessing Earth observation data, randomized control trials, and advanced modeling for global development and climate impact.
Research Design
Rerandomization procedures, effect heterogeneity, and leveraging images (e.g., satellite) as data sources.
Text-based AI Systems
Automated nonparametric text analysis, large language models, and language-based causal inferences.
Descriptive Representation & Political Economy
Understanding group-level representation worldwide; analyzing policy impacts.
CausalImages
R Package for performing causal inference with images, including Earth observation and biomedical data.
DescriptiveRepresentationCalculator
Tools for measuring descriptive representation across gender, ethnicity, religion, and more in global leadership data.
readme2
Enhanced automated content analysis for social science text data (with paper).
LinkOrgs
Linking organizational datasets using half-a-billion open-collaborated records.
For additional code packages, see Code Overview.
Explore large-scale data on Earth observation + RCTs, text-based sentiment disentanglement, organizational name-matching, and more:
Further details: Data Overview.