  This work showcases a new approach for causal discovery by leveraging user
experiments and recent advances in photo-realistic image editing, demonstrating
a potential of identifying causal factors and understanding complex systems
counterfactually. We introduce the beauty learning problem as an example, which
has been discussed metaphysically for centuries and been proved exists, is
quantifiable, and can be learned by deep models in our recent paper, where we
utilize a natural image generator coupled with user studies to infer causal
effects from facial semantics to beauty outcomes, the results of which also
align with existing empirical studies. We expect the proposed framework for a
broader application in causal inference.
