{"library":"econml","type":"library","category":null,"description":"EconML is a Python library for estimating Conditional Average Treatment Effects (CATEs) from observational or experimental data. It provides a suite of advanced machine learning methods, including Double Machine Learning (DML) and Causal Forests, to infer causal relationships and individual-level treatment effects. The current version is 0.16.0, and it maintains an active development pace with major updates and bugfix releases every few months.","language":"python","status":"active","version":"0.16.0","tags":["causal inference","machine learning","econometrics","treatment effects","causality","uplift modeling"],"last_verified":"Sat May 23","install":[{"cmd":"pip install econml","imports":["from econml.dml import CausalForestDML","from econml.dml import LinearDML","from econml.panel import DynamicDML"]},{"cmd":"pip install econml[ray]  # For distributed training with Ray\npip install econml[deepiv] # For DeepIV estimator with TensorFlow/Keras\npip install econml[all] # For all optional dependencies","imports":[]}],"homepage":"https://www.microsoft.com/en-us/research/project/econml/","github":"https://github.com/py-why/EconML","docs":"https://econml.azurewebsites.net/","changelog":null,"pypi":"https://pypi.org/project/econml/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":20.3,"avg_import_s":5.81,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/econml/compatibility"}}