Supplementary Material for " DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks "

A trivial method for satisfying FTU fairness, is to remove the protected attribute from downstream learners. We first provide a motivating example explaining why this is sub-optimal. We then follow this with an experiment on the Adult dataset. A.1 Example Defining fairness is task and data dependent. For example, let us assume two datasets are generated by the graphical models in Figure 1. Data generated by the top graph is considered fair: Education affects past experience (Resume), which together affect future job prospects (Job).