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...
| Главные авторы: | , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
Association for the Advancement of Artificial Intelligence
2021
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