Welcome to the visual repository of Connor T. Jerzak’s research. This gallery showcases high‑resolution figures from our interdisciplinary work at the University of Texas at Austin, panning causal inference with Earth‑observation imagery, generative machine‑learning models, experimental design, political representation, and social‑movement research. Each visualization comes from peer‑reviewed articles or conference proceedings—over a dozen publications in venues such as the Proceedings of Machine Learning Research (CLeaR), World Development, American Political Science Review, and forthcoming volumes from Cambridge University Press. Whether you’re exploring our machine‑learning methods for detecting treatment‑effect heterogeneity in satellite images, the CausalImages R package for analyzing biomedical and social science images, or our studies on descriptive representation and global development, this gallery offers insight into how data and theory come together to address real‑world policy questions.
