Causal inference from data. On some inadequacy problems of structures with hidden causes
The reliability of causal inference from data (by independence-based methods) is analyzed. We uncover some mechanisms which may result in model inadequacy due to sample bias and hidden variables. We detect some specific problems in recognition of direction of influence when some causes are hidden. I...
Saved in:
| Date: | 2020 |
|---|---|
| Main Author: | Balabanov, O.S. |
| Format: | Article |
| Language: | Ukrainian |
| Published: |
PROBLEMS IN PROGRAMMING
2020
|
| Subjects: | |
| Online Access: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/432 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Journal Title: | Problems in programming |
| Download file: | |
Institution
Problems in programmingSimilar Items
-
Knowledge discovery in data and causal models in analytical informatics
by: Balabanov, O.S.
Published: (2018) -
On the classes of causal networks, identifiable by simple independence tests
by: Balabanov, O.S.
Published: (2018) -
From temporal data to dynamic causal models
by: Balabanov, O.S.
Published: (2023) -
Big Data Analytics: principles, trends and tasks (a survey)
by: Balabanov, O.S.
Published: (2019) -
Principles and analytical tools for reconstruction of probabilistic dependency structures in special class
by: Balabanov, O.S.
Published: (2018)