CausalPy implements causal inference techniques such as difference-in-differences, synthetic control, regression discontinuity, and interrupted time series. It is built on top of PyMC for Bayesian-first estimation, enabling uncertainty-aware causal effect estimation. The package targets researchers and analysts working with quasi-experimental data across economics, social sciences, and policy evaluation.