mlsanityData to 7 Oct 2026

Recursive Causal Discovery

arXiv 2403.09300 ↗First posted 14 Mar 2024cs.LG stat.MLcited by 12

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Abstract (arXiv)

Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: limited data results in errors in statistical testing and the computational complexity of the learning task is daunting. This paper builds upon and extends four of our prior publications (Mokhtarian et al., 2021; Akbari et al., 2021; Mokhtarian et al., 2022, 2023a). These works introduced the concept of removable variables, which are the only variables that can be removed recursively for the purpose of causal discovery. Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively. This reduction not only minimizes conditioning sets in each conditional independence (CI) test, leading to fewer errors but also significantly decreases the number of required CI tests. The worst-case performances of these methods nearly match the lower bound. In this paper, we present a unified framework for the proposed algorithms, refined with additional details and enhancements for a coherent presentation. A comprehensive literature review is also included, comparing the computational complexity of our methods with existing approaches, showcasing their state-of-the-art efficiency. Another contribution of this paper is the release of RCD, a Python package that efficiently implements these algorithms. This package is designed for practitioners and researchers interested in applying these methods in practical scenarios. The package is available at github.com/ban-epfl/rcd, with comprehensive documentation provided at rcdpackage.com.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 35 of its 91 references that are in the map. First posted on arXiv 14 Mar 2024.

Builds on

35 of 91 references.

References · 35

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  4. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  5. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  6. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
  7. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  8. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
  9. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  10. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  11. 2022Interventional Causal Representation Learningcited by 140
  12. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
23 more
  1. 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 63
  2. 2013Learning Gaussian Networkscited by 62
  3. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  4. 2018Structural Agnostic Modeling: Adversarial Learning of Causal Graphscited by 48
  5. 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
  6. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  7. 2019Scaling structural learning with NO-BEARS to infer causal transcriptome networkscited by 39
  8. 2013A Discovery Algorithm for Directed Cyclic Graphscited by 33
  9. 2014Distinguishing cause from effect using observational data: methods and benchmarkscited by 33
  10. 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
  11. 2019Ordering-Based Causal Structure Learning in the Presence of Latent Variablescited by 26
  12. 2020Efficient Intervention Design for Causal Discovery with Latentscited by 22
  13. 2020A Recursive Markov Boundary-Based Approach to Causal Structure Learningcited by 21
  14. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  15. 2015Learning Directed Acyclic Graphs with Penalized Neighbourhood Regressioncited by 12
  16. 2021Learning Bayesian Networks in the Presence of Structural Side Informationcited by 11
  17. 2014Penalized Estimation of Directed Acyclic Graphs From Discrete Datacited by 10
  18. 2014Advances in Learning Bayesian Networks of Bounded Treewidthcited by 10
  19. 2022Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variablescited by 10
  20. 2021High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approachcited by 7
  21. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
  22. 2020Directed Graphical Models and Causal Discovery for Zero-Inflated Datacited by 6
  23. 2020Turbocharging Treewidth-Bounded Bayesian Network Structure Learningcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 6.

12 papers.

  1. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  2. 2025Does TabPFN Understand Causal Structures?cited by 4
  3. 2025SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graphcited by 3
  4. 2025Local Causal Discovery for Statistically Efficient Causal Inferencecited by 2
  5. 2025Graph Learning Is Suboptimal in Causal Banditscited by 2
  6. 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMscited by 1
  7. 2026Traceable Latent Variable Discovery Based on Multi-Agent Collaborationcited by 1
  8. 2026On the Number of Conditional Independence Tests in Constraint-based Causal Discoverycited by 1
  9. 2026Neural Autoregressive Flows for Markov Boundary Learningcited by 0
  10. 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variablescited by 0
  11. 2026Decoupled Causal Discoverycited by 0
  12. 2026Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Modelscited by 0