{"library":"decaf-synthetic-data","type":"library","category":null,"description":"DECAF (DEbiasing CAusal Fairness) is a Python library providing tools for generating synthetic data and debiasing causal effects. It implements methods to create synthetic datasets that capture complex causal relationships while mitigating various forms of bias, enabling researchers and practitioners to evaluate and develop fair causal inference models. Currently at version 0.1.7, the library is under active development with a focus on research-driven advancements.","language":"python","status":"active","version":"0.1.7","tags":["synthetic data","causal inference","fairness","debiasing","machine learning","research"],"last_verified":"Sun May 24","install":[{"cmd":"pip install decaf-synthetic-data","imports":["from decaf import DECAF","from decaf.synthetic_data import SyntheticData"]}],"homepage":null,"github":"https://github.com/trentkyono/DECAF","docs":null,"changelog":null,"pypi":"https://pypi.org/project/decaf-synthetic-data/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":10,"avg_install_s":90.9,"avg_import_s":20.16,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/decaf-synthetic-data/compatibility"}}