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.