Beauty Learning and Counterfactual Inference
This work addresses causal inference for complex systems like beauty perception, but it appears incremental as it builds on recent advances in image editing and user experiments.
The paper tackles the problem of causal discovery by introducing a beauty learning example, using a natural image generator and user studies to infer causal effects from facial semantics to beauty outcomes, with results aligning with existing empirical studies.
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.