EconML is a Python library for estimating heterogeneous treatment effects (CATE) from observational and experimental data using machine learning. It implements state-of-the-art causal inference methods including Double/Debiased Machine Learning (DML), Generalized Random Forests (GRF), Orthogonal Random Forests, Doubly Robust Learners, Instrumental Variable estimators (DMLIV, DRIV, OrthoIV), and Meta-Learners (S, T, X, DA). The library provides a unified scikit-learn-compatible API across all estimators with fit, effect, marginal_effect, and inference methods (confidence intervals, p-values). It includes advanced features such as cross-fitting, sample weights, missing data support, federated learning, sensitivity analysis, and model selection via RScorer. The repository also contains extensive documentation, notebooks, Monte Carlo tests, and a Cython-optimized tree implementation for performance.
StatsForecast is a production-grade Python library for large-scale univariate time series forecasting. It provides a unified API (fit/predict/forecast/cross_validation) over 40+ statistical models including AutoARIMA, AutoETS, AutoCES, AutoTheta, MSTL, TBATS, GARCH, and intermittent-demand methods (Croston, ADIDA, IMAPA). The core algorithms are implemented in C++ via pybind11 for performance, with Python wrappers handling model selection, prediction intervals, and conformal prediction. The library supports distributed execution on Spark, Dask, and Ray through a Fugue-based backend, and integrates with pandas, Polars, and MLFlow. While primarily an application library, the repository also contains extensive benchmarking experiments against R packages, Prophet, and NeuralProphet, as well as tutorials and competition notebooks, giving it a hybrid character. The codebase is well-structured with a clear separation of concerns (core API, model implementations, distributed backends, utilities) and includes a comprehensive test suite validated against R reference values.