mlsanityData to 7 Oct 2026

Invariant Ancestry Search

arXiv 2202.00913 ↗First posted 2 Feb 2022stat.ME cs.LGcited by 5

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Abstract (arXiv)

Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response variable. If the environments influence only few of the underlying mechanisms, the subset identified by invariant causal prediction (ICP), for example, may be small, or even empty. We introduce the concept of minimal invariance and propose invariant ancestry search (IAS). In its population version, IAS outputs a set which contains only ancestors of the response and is a superset of the output of ICP. When applied to data, corresponding guarantees hold asymptotically if the underlying test for invariance has asymptotic level and power. We develop scalable algorithms and perform experiments on simulated and real data.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 2 Feb 2022.

Builds on

18 of 52 references.

References · 18

  1. 2019Invariant Risk Minimizationcited by 1,832
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  4. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  5. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  6. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  7. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  8. 2017Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributionscited by 125
  9. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  10. 2020A causal framework for distribution generalizationcited by 68
  11. 2018Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolutioncited by 66
  12. 2017Invariant Causal Prediction for Sequential Datacited by 63
6 more
  1. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  2. 2018Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Frameworkcited by 16
  3. 2021Statistical Testing under Distributional Shiftscited by 6
  4. 2013An Algorithm for Finding Minimum d-Separating Sets in Belief Networkscited by 5
  5. 2021Variance Minimization in the Wasserstein Space for Invariant Causal Predictioncited by 5
  6. 2015On the Number of Minimal Separators in Graphscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.

5 papers.

  1. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  2. 2022Causal Discovery using Model Invariance through Knockoff Interventionscited by 4
  3. 2023Model-based causal feature selection for general response typescited by 3
  4. 2025Maximum Risk Minimization with Random Forestscited by 3
  5. 2024Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testingcited by 0