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.
pip install decaf-synthetic-dataVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to use `SyntheticData` to generate a base dataset, then how to initialize and train the `DECAF` model with this data, and finally, generate new synthetic samples from the trained model. This workflow is typical for evaluating debiasing strategies.
Always check the latest GitHub README and release notes (if any) before upgrading to new versions, and adapt your code as necessary.
Ensure `X`, `A`, and `Y` are NumPy arrays with appropriate dimensions (e.g., X as 2D, A and Y as 1D or 2D with one column). Review the quickstart for expected input formats.
Start with smaller datasets and fewer epochs to test your setup. Monitor resource usage (CPU/GPU, RAM) and consider optimizing hyperparameters or utilizing more powerful hardware for production-scale tasks.
Ensure you have installed the correct package: `pip install decaf-synthetic-data`.
Verify that your input arrays have compatible dimensions. For example, `X` should typically be `(n_samples, n_features)`, while `A` and `Y` could be `(n_samples,)` or `(n_samples, 1)`.
Check the official documentation or the `decaf/__init__.py` source code to confirm method names and their availability. The correct method to generate synthetic data from a trained model is `model.generate_synthetic_data()`.
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