DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

$van_der_Schaar \LAB$

Last update: Nov 24, 2022

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Overview

DECAF (DEbiasing CAusal Fairness)

Code Author: Trent Kyono

This repository contains the code used for the "DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks" paper(2021).

Installation

pip install -r requirements.txt
pip install .

Tests

You can run the tests using

pip install -r requirements_dev.txt
pip install .
pytest -vsx

decaf/DECAF.py - Synthetic data generator class - DECAF.
tests/run_example.py - Runs a nonlinear toy DAG example. The dag structure is stored in the dag_seed variable. The edge removal is stored in the bias_dict variable. See example usage in this file.

Examples

Base example on toy dag:

$ cd tests
$ python run_example.py

An example to run with a dataset size of 2000 for 300 epochs:

$ python run_example.py --datasize 2000 --epochs 300

Citing

@inproceedings{kyono2021decaf,
	title        = {DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks},
	author       = {Kyono, Trent and van Breugel, Boris and Berrevoets, Jeroen and van der Schaar, Mihaela},
	year         = 2021,
	booktitle    = {Conference on Neural Information Processing Systems(NeurIPS) 2021}
}

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DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

Related tags

Overview

DECAF (DEbiasing CAusal Fairness)

Installation

Tests

Contents

Examples

Citing

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Releases(v0.1.5)

v0.1.5(Jan 8, 2023)

v0.1.4(Dec 16, 2022)

v0.1.3(Nov 23, 2022)

v0.1.2(Nov 23, 2022)

Owner

van_der_Schaar \LAB

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