# Structure Learning for Cyclic Linear Causal Models

@inproceedings{Amndola2020StructureLF, title={Structure Learning for Cyclic Linear Causal Models}, author={Carlos Am{\'e}ndola and Philipp Dettling and Mathias Drton and Federica Onori and Jun Wu}, year={2020} }

We consider the problem of structure learning for linear causal models based on observational data. We treat models given by possibly cyclic mixed graphs, which allow for feedback loops and effects of latent confounders. Generalizing related work on bow-free acyclic graphs, we assume that the underlying graph is simple. This entails that any two observed variables can be related through at most one direct causal effect and that (confounding-induced) correlation between error terms in structural… CONTINUE READING

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 37 REFERENCES

## Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data

VIEW 7 EXCERPTS

HIGHLY INFLUENTIAL

## Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams

VIEW 9 EXCERPTS

HIGHLY INFLUENTIAL

## A Bayesian information criterion for singular models

VIEW 1 EXCERPT

## A Discovery Algorithm for Directed Cyclic Graphs

VIEW 1 EXCERPT

## Algebraic problems in structural equation modeling

VIEW 1 EXCERPT

## Causation, Prediction, and Search

VIEW 1 EXCERPT