Identifying Causal Effects Under Functional Dependencies
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911884433686528 |
|---|---|
| author | Chen, Yizuo Darwiche, Adnan |
| author_facet | Chen, Yizuo Darwiche, Adnan |
| contents | We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04919 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Causal Effects Under Functional Dependencies Chen, Yizuo Darwiche, Adnan Artificial Intelligence Machine Learning Symbolic Computation Methodology We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects. |
| title | Identifying Causal Effects Under Functional Dependencies |
| topic | Artificial Intelligence Machine Learning Symbolic Computation Methodology |
| url | https://arxiv.org/abs/2403.04919 |