  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.
