mlsanityData to 14 Oct 2026

Learning Causal Structures Using Regression Invariance

arXiv 1705.09644 ↗First posted 26 May 2017cs.LG cs.AI stat.MLcited by 32

Not on the map. Why?

Abstract (arXiv)

We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary. We introduce the idea of using the invariance of the functional relations of the variables to their causes across a set of environments. We define a notion of completeness for a causal inference algorithm in this setting and prove the existence of such algorithm by proposing the baseline algorithm. Additionally, we present an alternate algorithm that has significantly improved computational and sample complexity compared to the baseline algorithm. The experiment results show that the proposed algorithm outperforms the other existing algorithms.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 May 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 7; 2024: 3; 2025: 9; 2026: 1.

32 papers.

Sort:

  1. 2022Learning to Induce Causal Structurecited by 68
  2. 2022Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesiscited by 55
  3. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  4. 2023Environment Invariant Linear Least Squarescited by 17
  5. 2021Optimization-based Causal Estimation from Heterogenous Environmentscited by 16
  6. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  7. 2021Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentationcited by 10
  8. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 10
  9. 2023An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysiscited by 9
  10. 2023Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Datacited by 7
  11. 2024Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learningcited by 7
  12. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 7
20 more
  1. 2021Counterfactual Maximum Likelihood Estimation for Training Deep Networkscited by 5
  2. 2021Variance Minimization in the Wasserstein Space for Invariant Causal Predictioncited by 5
  3. 2023Boosting Differentiable Causal Discovery via Adaptive Sample Reweightingcited by 5
  4. 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Modelscited by 5
  5. 2021Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledgecited by 4
  6. 2022Learning Invariant Representations under General Interventions on the Responsecited by 4
  7. 2025Multi-Domain Causal Discovery in Bijective Causal Modelscited by 4
  8. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  9. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  10. 2022Differentiable Invariant Causal Discoverycited by 3
  11. 2024The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensingcited by 2
  12. 2024Causal Invariance Learning via Efficient Nonconvex Optimizationcited by 2
  13. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  14. 2025Causal Effect Identification in Heterogeneous Environments from Higher-Order Momentscited by 2
  15. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  16. 2023A step towards the applicability of algorithms based on invariant causal learning on observational datacited by 1
  17. 2021A Subsampling-Based Method for Causal Discovery on Discrete Datacited by 0
  18. 2023Learning Linear Gaussian Polytree Models with Interventionscited by 0
  19. 2025Causal Bayesian Optimization with Unknown Graphscited by 0
  20. 2025On the Identifiability of Causal Graphs with the Invariance Principlecited by 0