When performing causal discovery, assumptions have to be made on how the true causal mechanism corresponds to the underlying joint probability distribution. These assumptions are labeled as causal razors in this work. We review numerous causal razors that appeared in the literature, and offer a comprehensive logical comparison of them. In particular, we scrutinize an unpopular causal razor, namely parameter minimality, in multinomial causal models and its logical relations with other well-studied causal razors. Our logical result poses a dilemma in selecting a reasonable scoring criterion for score-based casual search algorithms.

翻译：在执行因果发现时，必须对真实因果机制如何对应潜在联合概率分布做出假设。这些假设在本工作中被标记为因果剃刀。我们回顾了文献中出现的众多因果剃刀，并对其进行了全面的逻辑比较。特别地，我们深入审视了一种不受欢迎的因果剃刀——即多项式因果模型中的参数最小性，以及它与其他经过充分研究的因果剃刀之间的逻辑关系。我们的逻辑结果为基于评分的因果搜索算法选择合理的评分标准提出了一个两难困境。