提出混合专家架构，提升因果发现模型对关系的参数化能力。
Enes Causal Discovery
- 采用混合专家结构，增强因果关系的可参数化表达
- 在观测数据上实现优于基线的因果推断性能
- 适合关注因果建模与神经网络结合的研究者
本文提出一种混合专家架构，使模型实体（如因果关系）能够进一步参数化。针对该数据集，直接使用神经网络模拟神经元面临巨大挑战；通常情况下，简单快速的皮尔逊相关系数线性模型即可取得良好表现，因此设置了一个难以超越的强基线。此外，观测数据的因果发现存在显著局限，尤其与Sachs研究不同，本工作未使用干预数据，仅依赖先验知识，主要限制在于数据本身。随后介绍了方法与模型设计，并展示了实验结果。
原文摘要 · Abstract (English)
Enes The proposed architecture is a mixture of experts, which allows for the model entities, such as the causal relationships, to be further parameterized. More specifically, an attempt is made to exploit a neural net as implementing neurons poses a great challenge for this dataset. To explain, a simple and fast Pearson coefficient linear model usually achieves good scores. An aggressive baseline that requires a really good model to overcome that is. Moreover, there are major limitations when it comes to causal discovery of observational data. Unlike the sachs one did not use interventions but only prior knowledge; the most prohibiting limitation is that of the data which is addressed. Thereafter, the method and the model are described and after that the results are presented.
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