mlsanityData to 7 Oct 2026

When Selection Meets Intervention: Additional Complexities in Causal Discovery

arXiv 2503.07302 ↗First posted 10 Mar 2025cs.LGcited by 9

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Abstract (arXiv)

We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug trial are usually patients of the relevant disease; A/B tests on mobile applications target existing users only, and gene perturbation studies typically focus on specific cell types, such as cancer cells. Ignoring this bias leads to incorrect causal discovery results. Even when recognized, the existing paradigm for interventional causal discovery still fails to address it. This is because subtle differences in when and where interventions happen can lead to significantly different statistical patterns. We capture this dynamic by introducing a graphical model that explicitly accounts for both the observed world (where interventions are applied) and the counterfactual world (where selection occurs while interventions have not been applied). We characterize the Markov property of the model, and propose a provably sound algorithm to identify causal relations as well as selection mechanisms up to the equivalence class, from data with soft interventions and unknown targets. Through synthetic and real-world experiments, we demonstrate that our algorithm effectively identifies true causal relations despite the presence of selection bias.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 28 of its 95 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

28 of 95 references.

References · 28

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  3. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  4. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  5. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  6. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  7. 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
  8. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  9. 2015Learning Causal Graphs with Small Interventionscited by 63
  10. 2013Causal Discovery from Changescited by 52
  11. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  12. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
16 more
  1. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  2. 2017Cost-Optimal Learning of Causal Graphscited by 44
  3. 2017Budgeted Experiment Design for Causal Structure Learningcited by 38
  4. 2015backShift: Learning causal cyclic graphs from unknown shift interventionscited by 37
  5. 2017Learning Causal Structures Using Regression Invariancecited by 32
  6. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  7. 2013Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphscited by 25
  8. 2016Ancestral Causal Inferencecited by 24
  9. 2013Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedurecited by 18
  10. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  11. 2022Local Constraint-Based Causal Discovery under Selection Biascited by 15
  12. 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal Viewcited by 11
  13. 2024Detecting and Identifying Selection Structure in Sequential Datacited by 9
  14. 2015Discovery and Visualization of Nonstationary Causal Modelscited by 7
  15. 2024Towards Complete Causal Explanation with Expert Knowledgecited by 6
  16. 2024Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Modelscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 5.

9 papers.

  1. 2025Latent Variable Causal Discovery under Selection Biascited by 6
  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. 2025Robust Causal Discovery under Imperfect Structural Constraintscited by 2
  5. 2025Causal Sensitivity Identification using Generative Learningcited by 0
  6. 2026Towards a holistic understanding of Selection Bias for Causal Effect Identificationcited by 0
  7. 2026Causal Modeling of Selection in Evolutioncited by 0
  8. 2026Identifying Structural Biases from Causal Mechanism Shiftscited by 0
  9. 2026CausalArena: Benchmarking Causal Discovery in the Foundation Model Eracited by 0