Meta learning for causal direction
The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent devel...
| Principais autores: | , , |
|---|---|
| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
Association for the Advancement of Artificial Intelligence
2021
|