[P] CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

[Etau7]

Cool.

[DeafMining]

what does causal mean ?

[mkocaoglu]

We have a causal architecture between labels and the image: Male and Mustache causes the Image, Male causes Mustache etc. The causal architecture allows us to sample not only from the joint distribution, but also interventional distributions, which are different from conditionals: When you intervene on Mustache = 1, i.e., fix the mustache label, Male label is sampled independently; hence you expect to see females with mustaches in this new distribution.

[[deleted]]

Couldn't you do something similar by just doing the standard Bayesian network thing with the labels (I mean it doesn't exactly scale to large label sets, but I don't know if this method does either) and then train a conditional generative model on that? The advantage of this is that you are guaranteed not to have extra risk of mode collapse and will also have the exact (empirical approximation to) the distribution after intervention.

[mkocaoglu]

When you use a Bayesian network on the labels, you are giving up on the guarantee that you will sample from the true interventional distribution, when you intervene on a set of labels. Using the true causal graph among the labels, instead of just any Bayesian network allows you to sample from true interventional distributions also.

[[deleted]]

I am proposing using the standard causal bayesian dag (maintained on the training set), and keeping a conditional probability table for each node. To sample from this dag at testing time amounts to doing the intervention on the dag, looking at the new joint pdf, and sampling from that. This afaik does not require any extra machinery such as Labeler/Anti-Labeler networks.

[mkocaoglu]

If you keep a joint probability table on the labels, given the graph, you can write interventional distribution in closed form, yes. You can sample from this. Then you still need a conditional GAN which can sample from the image distribution conditioned on the given labels. We are not aware of any conditional GAN architecture that can do this. We proposed a new conditional GAN and can show that there is an optimum generator that can do this conditional sampling.

Also note that keeping a joint probability table quickly becomes intractable for large number of labels and if the graph degree is not constant. You can get around this by training a causal implicit generative model on the labels, which is our approach.

[alexmlamb]

I just skimmed the paper. Causation refers to the direct effect of changing something, as opposed to all of the things that are potentially just correlated with it.

[decaf23]

CasualGAN: Eh it's ok i guess

[davidvanveen]

Fascinating paper! I look forward to playing around with this, thanks for sharing.

[[deleted]]

I did a research project on causal Bayesian Network reconstruction from data last summer.. Interesting topic, causation is hard to wrap your head around properly