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，它允许对诸如因果关系等模型实体进行进一步参数化。更具体地说，尝试利用神经网络来实现神经元对该数据集构成了巨大挑战。解释一下，简单快速的皮尔逊系数线性模型通常能取得良好分数。这是一个具有挑战性的基线，需要非常优秀的模型才能超越。此外，在观测数据的因果发现方面存在重大局限性。与Sachs不同，本研究未使用干预手段，仅依赖先验知识；其中最大的限制来自数据本身，这一问题已得到解决。随后，介绍了方法和模型，并给出了实验结果。