{"library":"causalml","type":"library","category":null,"description":"CausalML is a Python package for uplift modeling and causal inference with machine learning algorithms. It provides a variety of methods for causal inference in both experimental and observational settings, including meta-learners (S-Learner, T-Learner, X-Learner, R-Learner), tree-based methods (Causal Forest, Uplift Random Forest), and deep learning models. Current version: 0.16.0. Release cadence: irregular, with major updates approximately annually.","language":"python","status":"active","version":"0.16.0","tags":["causal-inference","uplift-modeling","machine-learning","python"],"last_verified":"Fri May 01","install":[{"cmd":"pip install causalml","imports":["from causalml.inference.meta import SLearner","from causalml.inference.forest import CausalForest","from causalml.propensity import set_rfub"]},{"cmd":"pip install causalml[all]","imports":[]}],"homepage":"https://causalml.readthedocs.io","github":"https://github.com/uber/causalml","docs":null,"changelog":null,"pypi":"https://pypi.org/project/causalml/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":null}