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：所提出的架构是一个专家混合模型，允许对模型实体（如因果关系）进行进一步参数化。具体而言，尝试利用神经网络实现神经元，这对该数据集构成了巨大挑战。需要说明的是，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。一个激进的基线方法需要真正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，该方法没有使用干预手段，仅依赖先验知识；最大的限制在于数据本身，而本文对此进行了处理。随后，描述了方法和模型，并展示了实验结果。