Corpus ID: 51882442

Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

@inproceedings{Yang2018CharacterizingAL,
  title={Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions},
  author={Karren D. Yang and Abigail Katoff and C. Uhler},
  booktitle={ICML},
  year={2018}
}
  • Karren D. Yang, Abigail Katoff, C. Uhler
  • Published in ICML 2018
  • Mathematics, Computer Science
  • We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. Hauser and B\"uhlmann (2012) previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. In this paper, we extend these… CONTINUE READING
    25 Citations

    Figures and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Permutation-Based Causal Structure Learning with Unknown Intervention Targets
    • 4
    • Highly Influenced
    • PDF
    Size of Interventional Markov Equivalence Classes in Random DAG Models
    • 1
    • PDF
    Distributional Invariances and Interventional Markov Equivalence for Mixed Graph Models
    • 1
    • Highly Influenced
    Learning and Sampling of Atomic Interventions from Observations
    • PDF
    Interventional Markov Equivalence for Mixed Graph Models
    • 1
    • Highly Influenced
    • PDF

    References

    SHOWING 1-10 OF 37 REFERENCES
    Causal discovery with continuous additive noise models
    • 227
    • PDF
    Permutation-based Causal Inference Algorithms with Interventions
    • 39
    • PDF
    Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs
    • 167
    • Highly Influential
    • PDF
    Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs
    • 50
    • Highly Influential
    • PDF
    Constraint-based causal discovery from multiple interventions over overlapping variable sets
    • 82
    • PDF
    Joint Causal Inference on Observational and Experimental Datasets
    • 12
    • Highly Influential
    • PDF
    Joint Causal Inference from Multiple Contexts
    • 39
    • PDF
    Equivalence and synthesis of causal models
    • 1,079
    • Highly Influential
    Local Characterizations of Causal Bayesian Networks
    • 21
    • Highly Influential
    • PDF
    Almost Optimal Intervention Sets for Causal Discovery
    • 29
    • PDF