EconML is a Python library for estimating heterogeneous treatment effects (CATE) from observational data, implementing state-of-the-art causal inference methods such as Double Machine Learning (DML), Doubly Robust (DR) estimation, Generalized Random Forests (GRF), Orthogonal Random Forests (ORF), and instrumental-variable (IV) approaches. The library provides a unified scikit-learn-like API with methods like fit, effect, effect_interval, and const_marginal_effect, and supports discrete/continuous treatments, sample weights, cross-fitting, Monte Carlo iterations, and multiple inference backends (bootstrap, statsmodels, debiased lasso). It also includes policy learning (PolicyTree/PolicyForest), interpretability (SHAP, tree-based interpreters), model validation (DRTester with BLP, calibration, uplift curves), and a high-level CausalAnalysis module for feature-level causal insights. The codebase is well-structured with clear separation of concerns: base estimator classes in _cate_estimator.py, cross-fitting orchestration in _ortho_learner.py, inference wrappers in inference/, and sklearn extensions for weighted/debiased linear models. External dependencies include scikit-learn, numpy, scipy, statsmodels, and optionally shap, ray, and azureml. The repository also contains extensive test suites, notebooks demonstrating usage on synthetic and real-world data, and prototype directories for experimental methods (deep DML/DR, dynamic panel DML). While primarily a software library, it has a strong research character with direct ties to academic papers and simulation-based validation.