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Causal Modeling of Selection in Evolution

arXiv 2606.05689 ↗First posted 4 Jun 2026cs.LGcited by 0

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Abstract (arXiv)

Understanding potential selection in data is crucial for causal discovery; we argue that "selection" in common narratives takes two forms, which we term static and evolutionary selection, respectively. Static selection refers to a one-shot filtering process where observed data consist of a subset of the population of interest, as in survey volunteer bias. Evolutionary selection, in contrast, operates through repeated rounds of differential fitness in reproduction, where observed data constitute the latest generation shaped by a historical trajectory, as in immune adaptation, antibiotic resistance, and social norm emergence. Existing methods largely conflate these two forms and rely on an identical graphical model of selection. We show that this model is valid for static settings but fails to characterize data under evolution, yielding false discovery results. To address this, we introduce a new model that specifically characterizes evolutionary selection, and develop a sound and complete procedure for identifying such models from data across one or multiple environments or generations. Experimental results validate the method's ability to uncover the relevant mechanisms underlying evolution from data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 71 references that are in the map. First posted on arXiv 4 Jun 2026.

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References · 20

  1. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  2. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  3. 2023Causal-learn: Causal Discovery in Pythoncited by 109
  4. 2018Causal Discovery in the Presence of Missing Datacited by 47
  5. 2013Context-Specific Independence in Bayesian Networkscited by 43
  6. 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Treescited by 38
  7. 2023A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variablescited by 33
  8. 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
  9. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 22
  10. 2022Local Constraint-Based Causal Discovery under Selection Biascited by 16
  11. 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal Viewcited by 11
  12. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 10
8 more
  1. 2024Detecting and Identifying Selection Structure in Sequential Datacited by 9
  2. 2025Reflection-Window Decoding: Text Generation with Selective Refinementcited by 8
  3. 2015Discovery and Visualization of Nonstationary Causal Modelscited by 7
  4. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 7
  5. 2025Latent Variable Causal Discovery under Selection Biascited by 7
  6. 2024Identifying Selections for Unsupervised Subtask Discoverycited by 4
  7. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  8. 2025Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lenscited by 2

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