I'm not aware of any techniques out there currently which convincingly address this problem. But I have been Googling around for hours and found the following resources.
This has a really nice idea that combines several softmax outputs (one for each discrete feature) with continuous ones at the end of the GAN.
This also mentions using a VAE to generate prototypes for counterfactual explanations, but I don't think it's as relevant.
MIT has a nice repository for doing this also, but it's in Tensorflow, and not easy to take apart for extending it for research etc.
I don't suppose anyone has any suggestions of which of these methods might be best? I'm leaning towards the first one, but surprisingly no one has a published a paper doing it, so I'd have to code it myself and it'd be hard to justify at a conference review process by citing an internet article and saying that "some guy on the internet said it worked well, so we did the same thing here".
I want to use such a generative model for research into explainable AI, but I've never surveyed this literature before, and it's pretty hectic. Thanks for any responses.
Source: Reddit Machine Learning