CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series
The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on obser...
Сохранить в:
| Главные авторы: | , , , |
|---|---|
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Wiley
2024-12-01
|
| Серии: | Advanced Intelligent Systems |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1002/aisy.202400181 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|