In this repo I create methods in causal inference and Bayesian nonparametrics that: 1. can be used in practice, featuring scalable implementations that facilitate their application to real data; 2. are designed to handle the complexity inherent in real data without making naive assumptions; and 3. have exceptional predictive accuracy, even as they boast other desirable features like interpretability and uncertainty quantification.