Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beauty Learning and Counterfactual Inference   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.

36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics