mlsanityData to 14 Oct 2026

Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders

arXiv 2501.10124 ↗First posted 17 Jan 2025cs.LGcited by 7

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Abstract (arXiv)

Gene regulatory network inference (GRNI) aims to discover how genes causally regulate each other from gene expression data. It is well-known that statistical dependencies in observed data do not necessarily imply causation, as spurious dependencies may arise from latent confounders, such as non-coding RNAs. Numerous GRNI methods have thus been proposed to address this confounding issue. However, dependencies may also result from selection--only cells satisfying certain survival or inclusion criteria are observed--while these selection-induced spurious dependencies are frequently overlooked in gene expression data analyses. In this work, we show that such selection is ubiquitous and, when ignored or conflated with true regulations, can lead to flawed causal interpretation and misguided intervention recommendations. To address this challenge, a fundamental question arises: can we distinguish dependencies due to regulation, confounding, and crucially, selection? We show that gene perturbations offer a simple yet effective answer: selection-induced dependencies are symmetric under perturbation, while those from regulation or confounding are not. Building on this motivation, we propose GISL (Gene regulatory network Inference in the presence of Selection bias and Latent confounders), a principled algorithm that leverages perturbation data to uncover both true gene regulatory relations and non-regulatory mechanisms of selection and confounding up to the equivalence class. Experiments on synthetic and real-world gene expression data demonstrate the effectiveness of our method.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 21 of its 106 references that are in the map. First posted on arXiv 17 Jan 2025.

Builds on

21 of 106 references.

References · 21

  1. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  2. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  3. 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
  4. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  5. 2013Causal Discovery from Changescited by 52
  6. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  7. 2017Cost-Optimal Learning of Causal Graphscited by 44
  8. 2017Learning Causal Structures Using Regression Invariancecited by 32
  9. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 31
  10. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  11. 2016Ancestral Causal Inferencecited by 24
  12. 2021Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Biascited by 21
9 more
  1. 2013Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedurecited by 18
  2. 2021Matching a Desired Causal State via Shift Interventionscited by 16
  3. 2022Local Constraint-Based Causal Discovery under Selection Biascited by 16
  4. 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulantscited by 16
  5. 2024Federated Causal Discovery from Heterogeneous Datacited by 15
  6. 2023Nonlinear Causal Discovery with Confounderscited by 9
  7. 2021FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounderscited by 6
  8. 2024psiDAG: Projected Stochastic Approximation Iteration for DAG Structure Learningcited by 4
  9. 2020Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounderscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 13
  2. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  3. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  4. 2025PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traitscited by 0
  5. 2026Towards a holistic understanding of Selection Bias for Causal Effect Identificationcited by 0
  6. 2026Causal Modeling of Selection in Evolutioncited by 0
  7. 2026Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approachcited by 0