Jerzak Labs

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.

Research Areas

• 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.

Featured Repositories

• 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.

Data Assets

Explore large-scale data on Earth observation + RCTs, text-based sentiment disentanglement, organizational name-matching, and more:

• HumanDisentangledText
• LinkOrgs
• ImageHeterogeneity

Further details: Data Overview.

Contact

• Email: [email protected]
• Website: ConnorJerzak.com
• GitHub: GitHub.com/cjerzak