mlsanityData to 7 Oct 2026

Local Constraint-Based Causal Discovery under Selection Bias

arXiv 2203.01848 ↗First posted 3 Mar 2022cs.LG cs.AI stat.MLcited by 15

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Abstract (arXiv)

We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present. While the seminal FCI algorithm is sound and complete in this setup, no criterion for the causal interpretation of its output under selection bias is presently known. We focus instead on local patterns of independence relations, where we find no sound method for only three variable that can include background knowledge. Y-Structure patterns are shown to be sound in predicting causal relations from data under selection bias, where cycles may be present. We introduce a finite-sample scoring rule for Y-Structures that is shown to successfully predict causal relations in simulation experiments that include selection mechanisms. On real-world microarray data, we show that a Y-Structure variant performs well across different datasets, potentially circumventing spurious correlations due to selection bias.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 3 Mar 2022.

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8 of 38 references.

References · 8

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  3. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  4. 2013Learning Sparse Causal Models is not NP-hardcited by 48
  5. 2018Causal Discovery in the Presence of Missing Datacited by 46
  6. 2017Markov Properties for Graphical Models with Cycles and Latent Variablescited by 32
  7. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  8. 2019Boosting Local Causal Discovery in High-Dimensional Expression Datacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 5; 2026: 4.

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  1. 2023Discovering Causal Relations and Equations from Datacited by 33
  2. 2024Local Causal Structure Learning in the Presence of Latent Variablescited by 11
  3. 2023Local Causal Discovery for Estimating Causal Effectscited by 10
  4. 2023Towards Practical Federated Causal Structure Learningcited by 9
  5. 2024Detecting and Identifying Selection Structure in Sequential Datacited by 9
  6. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  7. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 6
  8. 2025Latent Variable Causal Discovery under Selection Biascited by 6
  9. 2024Identifying Selections for Unsupervised Subtask Discoverycited by 4
  10. 2025Amortized Conditional Independence Testingcited by 1
  11. 2025Can LLMs Assist Expert Elicitation for Probabilistic Causal Modeling?cited by 1
  12. 2026From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variablescited by 0
3 more
  1. 2026Towards a holistic understanding of Selection Bias for Causal Effect Identificationcited by 0
  2. 2026Causal Modeling of Selection in Evolutioncited by 0
  3. 2026Identifying Structural Biases from Causal Mechanism Shiftscited by 0