DECAF Synthetic Data

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.