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.

翻译：所提出的架构是一种专家混合模型，允许对模型实体（如因果关系）进行进一步参数化。更具体地说，尝试利用神经网络在该数据集上实现神经元建模存在巨大挑战。为此，一个简单快速的皮尔逊系数线性模型通常能取得良好得分。这是一个需要非常优秀的模型才能超越的激进基线。此外，在观测数据的因果发现方面存在重大限制。与使用干预手段的Sachs研究不同，本工作仅利用先验知识；其中最严重的限制来自数据本身，这一问题已得到解决。随后，本文描述了方法与模型，并展示了实验结果。