# 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} }

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

#### Figures and Topics from this paper

25 Citations

Permutation-Based Causal Structure Learning with Unknown Intervention Targets

- Computer Science, Mathematics
- UAI
- 2020

4 Highly Influenced- PDF

Interventional Experiment Design for Causal Structure Learning

- Computer Science, Mathematics
- ArXiv
- 2019

2- PDF

Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning

- Psychology, Computer Science
- NeurIPS
- 2020

- PDF

Size of Interventional Markov Equivalence Classes in Random DAG Models

- Mathematics, Computer Science
- AISTATS
- 2019

1- PDF

Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions

- Computer Science
- NeurIPS
- 2019

11- PDF

Distributional Invariances and Interventional Markov Equivalence for Mixed Graph Models

- Mathematics
- 2019

1 Highly Influenced

Efficiently Learning and Sampling Interventional Distributions from Observations

- Computer Science
- ArXiv
- 2020

2

Learning and Sampling of Atomic Interventions from Observations

- Computer Science, Mathematics
- ICML
- 2020

- PDF

Differentiable Causal Discovery from Interventional Data

- Computer Science, Mathematics
- NeurIPS
- 2020

1- PDF

#### References

SHOWING 1-10 OF 37 REFERENCES

Causal discovery with continuous additive noise models

- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 2014

227- PDF

Permutation-based Causal Inference Algorithms with Interventions

- Computer Science, Mathematics
- NIPS
- 2017

39- PDF

Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs

- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 2012

167 Highly Influential- PDF

Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs

- Mathematics
- 2013

50 Highly Influential- PDF

Constraint-based causal discovery from multiple interventions over overlapping variable sets

- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 2015

82- PDF

Joint Causal Inference on Observational and Experimental Datasets

- Mathematics, Computer Science
- ArXiv
- 2016

12 Highly Influential- PDF

Joint Causal Inference from Multiple Contexts

- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 2020

39- PDF

Local Characterizations of Causal Bayesian Networks

- Mathematics, Computer Science
- GKR
- 2011

21 Highly Influential- PDF