mlsanityData to 7 Oct 2026

Local Causal Discovery with Background Knowledge

arXiv 2408.07890 ↗First posted 15 Aug 2024stat.ML cs.LGcited by 5

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Abstract (arXiv)

Causality plays a pivotal role in various fields of study. Based on the framework of causal graphical models, previous works have proposed identifying whether a variable is a cause or non-cause of a target in every Markov equivalent graph solely by learning a local structure. However, the presence of prior knowledge, often represented as a partially known causal graph, is common in many causal modeling applications. Leveraging this prior knowledge allows for the further identification of causal relationships. In this paper, we first propose a method for learning the local structure using all types of causal background knowledge, including direct causal information, non-ancestral information and ancestral information. Then we introduce criteria for identifying causal relationships based solely on the local structure in the presence of prior knowledge. We also apply out method to fair machine learning, and experiments involving local structure learning, causal relationship identification, and fair machine learning demonstrate that our method is both effective and efficient.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 71 references that are in the map. First posted on arXiv 15 Aug 2024.

Builds on

19 of 71 references.

References · 19

  1. 2017Counterfactual Fairnesscited by 802
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2018Fair Clustering Through Fairletscited by 176
  4. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  5. 2019Fair Algorithms for Clusteringcited by 98
  6. 2019Scalable Fair Clusteringcited by 78
  7. 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 75
  8. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  9. 2014Estimating the effect of joint interventions from observational data in sparse high-dimensional settingscited by 20
  10. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  11. 2020Minimal enumeration of all possible total effects in a Markov equivalence classcited by 12
  12. 2020FairNN- Conjoint Learning of Fair Representations for Fair Decisionscited by 11
7 more
  1. 2024Local Causal Structure Learning in the Presence of Latent Variablescited by 11
  2. 2023Local Causal Discovery for Estimating Causal Effectscited by 10
  3. 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
  4. 2020Active Learning of Causal Structures with Deep Reinforcement Learningcited by 6
  5. 2021A Local Method for Identifying Causal Relations under Markov Equivalencecited by 6
  6. 2023Efficient Enumeration of Markov Equivalent DAGscited by 6
  7. 2023Structural restrictions in local causal discovery: identifying direct causes of a target variablecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discoverycited by 0
  2. 2026Knowledge-Informed Local Causal Discovery of Optimal Adjustment Setscited by 0
  3. 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0
  4. 2026Resilient Concurrent Causal Discovery for Topological Event Sequencescited by 0
  5. 2026xWhyL: Causal Interactive Learningcited by 0