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...

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Bibliografski detalji

Glavni autori:	Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Format:	Artigo
Jezik:	Inglês
Izdano:	Wiley 2024-12-01
Serija:	Advanced Intelligent Systems
Teme:	causal robotics observations and interventions‐based causal discoveries time series
Online pristup:	https://doi.org/10.1002/aisy.202400181
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