mlsanityData to 7 Oct 2026

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

arXiv 2205.10083 ↗First posted 20 May 2022cs.LG cs.AIcited by 7

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Abstract (arXiv)

We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skeleton of causal graphs with cycles may not be possible from merely the observational distribution. Furthermore, intervening on a variable in such graphs does not necessarily lead to orienting all the edges incident to it. In this paper, we propose an experiment design approach that can learn both cyclic and acyclic graphs and hence, unifies the task of experiment design for both types of graphs. We provide a lower bound on the number of experiments required to guarantee the unique identification of the causal graph in the worst case, showing that the proposed approach is order-optimal in terms of the number of experiments up to an additive logarithmic term. Moreover, we extend our result to the setting where the size of each experiment is bounded by a constant. For this case, we show that our approach is optimal in terms of the size of the largest experiment required for uniquely identifying the causal graph in the worst case.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 21 of its 44 references that are in the map. First posted on arXiv 20 May 2022.

Builds on

21 of 44 references.

References · 21

  1. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  2. 2015Learning Causal Graphs with Small Interventionscited by 63
  3. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  4. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  5. 2017Cost-Optimal Learning of Causal Graphscited by 44
  6. 2017Budgeted Experiment Design for Causal Structure Learningcited by 38
  7. 2020Active Structure Learning of Causal DAGs via Directed Clique Treecited by 38
  8. 2018Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounderscited by 35
  9. 2013A Discovery Algorithm for Directed Cyclic Graphscited by 33
  10. 2017Markov Properties for Graphical Models with Cycles and Latent Variablescited by 32
  11. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  12. 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
9 more
  1. 2018Experimental Design for Cost-Aware Learning of Causal Graphscited by 25
  2. 2020A Recursive Markov Boundary-Based Approach to Causal Structure Learningcited by 21
  3. 2020Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGscited by 14
  4. 2021Learning Bayesian Networks in the Presence of Structural Side Informationcited by 11
  5. 2020LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experimentscited by 9
  6. 2022Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applicationscited by 9
  7. 2018Counting and Sampling from Markov Equivalent DAGs Using Clique Treescited by 8
  8. 2019Interventional Experiment Design for Causal Structure Learningcited by 8
  9. 2013A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Modelscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  2. 2024Recursive Causal Discoverycited by 12
  3. 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelscited by 4
  4. 2024Interventional Causal Discovery in a Mixture of DAGscited by 3
  5. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 1
  6. 2025Design of Experiment for Discovering Directed Mixed Graphcited by 0
  7. 2026SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targetscited by 0