DoWhy is a mature Python library for causal inference that implements a systematic four-step workflow: model (encode causal graph), identify (derive estimand), estimate (compute causal effect), and refute (validate robustness). The library supports two major frameworks: (1) classical potential-outcomes-based estimation with ATE/ATT/CATE via backdoor, frontdoor, instrumental variables, and mediation analysis, leveraging multiple estimators including linear regression, propensity score methods, doubly robust, instrumental variable, regression discontinuity, and integrations with EconML and CausalML; (2) Graphical Causal Model (GCM) based inference for what-if analysis, counterfactual sampling, distribution change attribution, arrow strength, intrinsic causal influence, anomaly attribution, and root cause analysis. The architecture is modular with abstract base classes for estimators, refuters, interpreters, and GCM components. The main entry point is the CausalModel class, which orchestrates identification, estimation, and refutation via a consistent API. Key dependencies include scikit-learn, statsmodels, networkx, sympy, pandas, numpy, and optional integrations with Torch, EconML, Causal-learn, and TabPFN. The library is production-ready with comprehensive test suites, CI/CD, and extensive documentation, and is actively maintained with a large contributor base.

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.