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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400181 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850243578481082368 |
|---|---|
| author | Luca Castri Sariah Mghames Marc Hanheide Nicola Bellotto |
| author_facet | Luca Castri Sariah Mghames Marc Hanheide Nicola Bellotto |
| author_sort | Luca Castri |
| collection | DOAJ |
| description | 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 observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time‐series data. The use of interventional data in the causal analysis is crucial for real‐world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well‐known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow. |
| format | Article |
| id | doaj-art-059f520a12c34db5b05dc60a0c04fc76 |
| institution | OA Journals |
| issn | 2640-4567 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-059f520a12c34db5b05dc60a0c04fc762025-08-20T01:59:57ZengWileyAdvanced Intelligent Systems2640-45672024-12-01612n/an/a10.1002/aisy.202400181CAnDOIT: Causal Discovery with Observational and Interventional Data from Time SeriesLuca Castri0Sariah Mghames1Marc Hanheide2Nicola Bellotto3L‐CAS, School of Engineering & Physical Sciences University of Lincoln Lincoln LN6 7TS UKL‐CAS, School of Engineering & Physical Sciences University of Lincoln Lincoln LN6 7TS UKL‐CAS, School of Engineering & Physical Sciences University of Lincoln Lincoln LN6 7TS UKL‐CAS, School of Engineering & Physical Sciences University of Lincoln Lincoln LN6 7TS UKThe 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 observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time‐series data. The use of interventional data in the causal analysis is crucial for real‐world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well‐known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.https://doi.org/10.1002/aisy.202400181causal roboticsobservations and interventions‐based causal discoveriestime series |
| spellingShingle | Luca Castri Sariah Mghames Marc Hanheide Nicola Bellotto CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series Advanced Intelligent Systems causal robotics observations and interventions‐based causal discoveries time series |
| title | CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series |
| title_full | CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series |
| title_fullStr | CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series |
| title_full_unstemmed | CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series |
| title_short | CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series |
| title_sort | candoit causal discovery with observational and interventional data from time series |
| topic | causal robotics observations and interventions‐based causal discoveries time series |
| url | https://doi.org/10.1002/aisy.202400181 |
| work_keys_str_mv | AT lucacastri candoitcausaldiscoverywithobservationalandinterventionaldatafromtimeseries AT sariahmghames candoitcausaldiscoverywithobservationalandinterventionaldatafromtimeseries AT marchanheide candoitcausaldiscoverywithobservationalandinterventionaldatafromtimeseries AT nicolabellotto candoitcausaldiscoverywithobservationalandinterventionaldatafromtimeseries |