Name
Title | ||
|---|---|---|
Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders |
Abstract | ||
|---|---|---|
Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders. RCD finally produces a causal graph where a bidirected arrow indicates the pair of variables that have the same latent confounders and a directed arrow indicates the causal direction of a pair of variables that are not affected by the same latent confounder. The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s41060-021-00282-0 | International Journal of Data Science and Analytics |
Keywords | DocType | Volume |
Causal discovery, Causal structures, Latent confounders | Journal | 13 |
Issue | ISSN | Citations |
2 | 2364-415X | 0 |
PageRank | References | Authors |
0.34 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Nicholas Maeda | 1 | 0 | 0.34 |
| Shohei Shimizu | 2 | 492 | 45.80 |