mlsanityData to 7 Oct 2026

Characterization and Learning of Causal Graphs from Hard Interventions

arXiv 2505.01037 ↗First posted 2 May 2025stat.ML cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

A fundamental challenge in the empirical sciences involves uncovering causal structure through observation and experimentation. Causal discovery entails linking the conditional independence (CI) invariances in observational data to their corresponding graphical constraints via d-separation. In this paper, we consider a general setting where we have access to data from multiple experimental distributions resulting from hard interventions, as well as potentially from an observational distribution. By comparing different interventional distributions, we propose a set of graphical constraints that are fundamentally linked to Pearl's do-calculus within the framework of hard interventions. These graphical constraints associate each graphical structure with a set of interventional distributions that are consistent with the rules of do-calculus. We characterize the interventional equivalence class of causal graphs with latent variables and introduce a graphical representation that can be used to determine whether two causal graphs are interventionally equivalent, i.e., whether they are associated with the same family of hard interventional distributions, where the elements of the family are indistinguishable using the invariances from do-calculus. We also propose a learning algorithm to integrate multiple datasets from hard interventions, introducing new orientation rules. The learning objective is a tuple of augmented graphs which entails a set of causal graphs. We also prove the soundness of the proposed algorithm.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 69 references that are in the map. First posted on arXiv 2 May 2025.

Builds on

27 of 69 references.

References · 27

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  3. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  4. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  5. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  6. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  7. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  8. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  9. 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
  10. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  11. 2015Learning Causal Graphs with Small Interventionscited by 63
  12. 2022Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesiscited by 54
15 more
  1. 2013Causal Discovery from Changescited by 52
  2. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  3. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  4. 2013Strong Completeness and Faithfulness in Bayesian Networkscited by 44
  5. 2017Budgeted Experiment Design for Causal Structure Learningcited by 38
  6. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  7. 2017Learning Causal Structures Using Regression Invariancecited by 32
  8. 2018Learning and Testing Causal Models with Interventionscited by 29
  9. 2013An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanationcited by 17
  10. 2020Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGscited by 14
  11. 2022Revisiting the General Identifiability Problemcited by 13
  12. 2020Intervention Efficient Algorithms for Approximate Learning of Causal Graphscited by 7
  13. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4
  14. 2021Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencingcited by 2
  15. 2023Bayesian causal discovery from unknown general interventionscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  2. 2026Relaxing Faithfulness with Intervention-Only Causal Discoverycited by 0