On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models
Not on the map. Why?
Abstract (arXiv)
Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, however, can be unreliable, and algorithms are sensitive to errors and violated assumptions. Often, there are tests that were not used in the construction of the graph. In this work, we show that these redundant tests have the potential to detect or sometimes correct errors in the learned model. But we further show that not all tests contain this additional information and that such redundant tests have to be applied with care. Precisely, we argue that the conditional (in)dependence statements that hold for every probability distribution are unlikely to detect and correct errors - in contrast to those that follow only from graphical assumptions.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 69 references that are in the map. First posted on arXiv 12 Feb 2025.
Builds on
22 of 69 references.
References · 22
- 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
- 2023Causal-learn: Causal Discovery in Pythoncited by 109
- 2013Causal Networks: Semantics and Expressivenesscited by 67
- 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 64
- 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Treescited by 38
- 2022Causal Inference Through the Structural Causal Marginal Problemcited by 23
- 2023pgmpy: A Python Toolkit for Bayesian Networkscited by 23
- 2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
- 2021Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Biascited by 21
- 2020Near-Optimal Learning of Tree-Structured Distributions by Chow-Liucited by 20
- 2024Causal Discovery with Fewer Conditional Independence Testscited by 13
- 2015Learning Directed Acyclic Graphs with Penalized Neighbourhood Regressioncited by 12
10 more
- 2016Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-valuecited by 11
- 2023Characterization and Learning of Causal Graphs with Small Conditioning Setscited by 11
- 2023Toward Falsifying Causal Graphs Using a Permutation-Based Testcited by 11
- 2024Separation-based distance measures for causal graphscited by 9
- 2016Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Valuescited by 8
- 2020Recovering Causal Structures from Low-Order Conditional Independenciescited by 6
- 2023Reinterpreting causal discovery as the task of predicting unobserved joint statisticscited by 4
- 2024Argumentative Causal Discoverycited by 3
- 2024Cross-validating causal discovery via Leave-One-Variable-Outcited by 3
- 2024Structural perspective on constraint-based learning of Markov networkscited by 2
Built on by
Citing papers per year: 2026: 3.
3 papers.