Causal Razors
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 59 references that are in the map. First posted on arXiv 20 Feb 2023.
Builds on
12 of 59 references.
References · 12
- 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
- 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
- 2013Causal Networks: Semantics and Expressivenesscited by 67
- 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 63
- 2013A Transformational Characterization of Equivalent Bayesian Network Structurescited by 41
- 2013On the Logic of Causal Modelscited by 19
- 2022Reliable Causal Discovery with Improved Exact Search and Weaker Assumptionscited by 19
- 2013Markovian acyclic directed mixed graphs for discrete datacited by 10
- 2015A Uniformly Consistent Estimator of Causal Effects under the k-Triangle-Faithfulness Assumptioncited by 10
- 2018On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumptioncited by 6
- 2022The m-connecting imset and factorization for ADMG modelscited by 4
- 2019ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptionscited by 3
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