When we work with timeseries data containing multiple features eg. a sensor data. we can detect anomalies using cluster, supervised and semi supervised based approaches (Eg. Isolation, Autoencoder Etc), but those approaches only detect anomalies and not their root causes. Example certain event which has occurred at time 1, might cause ripple effect which can in turn lead to anomalies at time 2.
How can we perform such operations, in general how can we extend our anomalies models to detect root causes or are their any algorithms available given anomalies can pip point possible root causes.