  Machine learning models have been criticized for reflecting unfair biases in
the training data. Instead of solving for this by introducing fair learning
algorithms directly, we focus on generating fair synthetic data, such that any
downstream learner is fair. Generating fair synthetic data from unfair data -
while remaining truthful to the underlying data-generating process (DGP) - is
non-trivial. In this paper, we introduce DECAF: a GAN-based fair synthetic data
generator for tabular data. With DECAF we embed the DGP explicitly as a
structural causal model in the input layers of the generator, allowing each
variable to be reconstructed conditioned on its causal parents. This procedure
enables inference time debiasing, where biased edges can be strategically
removed for satisfying user-defined fairness requirements. The DECAF framework
is versatile and compatible with several popular definitions of fairness. In
our experiments, we show that DECAF successfully removes undesired bias and -
in contrast to existing methods - is capable of generating high-quality
synthetic data. Furthermore, we provide theoretical guarantees on the
generator's convergence and the fairness of downstream models.
