Visualizing Causal Assignment Trees for CSDiD and DR-DDD Designs // Tools for constructing, labeling, and visualizing Causal Assignment Trees (CATs) in settings with staggered adoption. Supports Callaway and Sant'Anna difference-in-differences (CSDiD) and doubly robust difference-in-difference-differences (DR-DDD) designs. The package helps clarify treatment timing, never-treated vs. not-yet-treated composition, and subgroup structure, and produces publication-quality diagrams and summary tables. Current functionality focuses on data-to-node mapping, node counts, cohort-year summaries, and high-quality tree plots suitable for empirical applications prior to estimation. Methods are based on Callaway and Sant'Anna (2021) doi:10.1016/j.jeconom.2020.12.001, Sant'Anna and Zhao (2020) doi:10.1016/j.jeconom.2020.06.003, and Kilanko (2026) https://github.com/VictorKilanko/catviz.