# Causal Razors

arXiv: https://arxiv.org/abs/2302.10331 · HTML: https://mlsanity.com/paper/2302.10331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Causal Inference and Causal Explanation with Background Knowledge](https://mlsanity.com/paper/1302.4972/) (2013)
- [Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery](https://mlsanity.com/paper/1702.03877/) (2017)
- [Causal Networks: Semantics and Expressiveness](https://mlsanity.com/paper/1304.2379/) (2013)
- [Greedy Relaxations of the Sparsest Permutation Algorithm](https://mlsanity.com/paper/2206.05421/) (2022)
- [A Transformational Characterization of Equivalent Bayesian Network Structures](https://mlsanity.com/paper/1302.4938/) (2013)
- [On the Logic of Causal Models](https://mlsanity.com/paper/1304.2355/) (2013)
- [Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions](https://mlsanity.com/paper/2201.05666/) (2022)
- [Markovian acyclic directed mixed graphs for discrete data](https://mlsanity.com/paper/1301.6624/) (2013)
- [A Uniformly Consistent Estimator of Causal Effects under the k-Triangle-Faithfulness Assumption](https://mlsanity.com/paper/1502.00829/) (2015)
- [On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption](https://mlsanity.com/paper/1802.07051/) (2018)
- [The m-connecting imset and factorization for ADMG models](https://mlsanity.com/paper/2207.08963/) (2022)
- [ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions](https://mlsanity.com/paper/1906.02385/) (2019)

## Built on by (most cited first, 1 of 1)

- [Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set](https://mlsanity.com/paper/2411.08141/) (2024)

## 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.