mlsanityData to 14 Oct 2026

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

arXiv 2211.14897 ↗First posted 27 Nov 2022stat.ME stat.CO stat.MLcited by 12

Not on the map. Why?

Abstract (arXiv)

We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships in the system. Different models may entail the same distributions, offering competing causal explanations for the given observations. We fully characterize this equivalence class and offer identifiability results, which we use to derive a greedy algorithm called GnIES to recover the equivalence class of the data-generating model without knowledge of the intervention targets. In addition, we develop a novel procedure to generate semi-synthetic data sets with known causal ground truth but distributions closely resembling those of a real data set of choice. We leverage this procedure and evaluate the performance of GnIES on an array of synthetic and semi-synthetic data sets, and real data from a biological system and a tightly controlled physical system. We provide, in the Python packages gnies and sempler, implementations of GnIES and our semi-synthetic data generation procedure.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 33 of its 68 references that are in the map. First posted on arXiv 27 Nov 2022.

Builds on

33 of 68 references.

References · 33

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
  3. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  4. 2019Gradient-Based Neural DAG Learningcited by 227
  5. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  6. 2019Learning Sparse Nonparametric DAGscited by 207
  7. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  8. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  9. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
  10. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  11. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  12. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
21 more
  1. 2018Invariance, Causality and Robustnesscited by 123
  2. 2018Anchor regression: heterogeneous data meets causalitycited by 118
  3. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  4. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  5. 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
  6. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  7. 2017Invariant Causal Prediction for Sequential Datacited by 63
  8. 2013Causal Discovery from Changescited by 52
  9. 2015High-dimensional consistency in score-based and hybrid structure learningcited by 46
  10. 2013A Transformational Characterization of Equivalent Bayesian Network Structurescited by 41
  11. 2020Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regressioncited by 37
  12. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  13. 2017Learning Causal Structures Using Regression Invariancecited by 32
  14. 2019Stabilizing Variable Selection and Regressioncited by 31
  15. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  16. 2013Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphscited by 25
  17. 2016Ancestral Causal Inferencecited by 24
  18. 2013Cyclic Causal Discovery from Continuous Equilibrium Datacited by 21
  19. 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
  20. 2015Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluationscited by 14
  21. 2022Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 4; 2026: 3.

12 papers.

  1. 2023Learning Linear Causal Representations from Interventions under General Nonlinear Mixingcited by 92
  2. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  3. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
  4. 2026Coarsening Causal DAG Modelscited by 4
  5. 2023Bayesian causal discovery from unknown general interventionscited by 2
  6. 2024Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guaranteescited by 1
  7. 2025Bayesian Hierarchical Invariant Predictioncited by 1
  8. 2025Less Greedy Equivalence Searchcited by 1
  9. 2025Intervening to Learn and Compose Causally Disentangled Representationscited by 1
  10. 2026I-FLOP: Fast Learning of Order and Parents from Interventional Datacited by 1
  11. 2025Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral samplingcited by 0
  12. 2026Score-Based Learning of Cluster DAGs from Interventionscited by 0