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:在执行因果探索时,必须对真实因果机制如何对应于潜在联合概率分布作出假设。这些假设在本文中被标记为因果剃刀。我们回顾了出现在文献中的许多因果剃刀,并对它们进行了全面的逻辑比较。特别地,我们对多项式因果模型中一个不常见且未被关注的因果剃刀-参数最小性进行了审查,以及它与其他广泛研究的因果剃刀之间的逻辑关系。我们的逻辑结果在选择评分基准以进行基于评分的因果搜索算法时存在困境。