did you already read our white paper about predictive maintenance. There we presented something really similar. The key is the lag column node which enables you to use information from previous data rows.
Anomaly Detection I: Time Alignment and Visualization for Anomaly Detection (2015)
Anomaly Detection II: Anomaly Detection in Predictive Maintenance with Time Series Analysis (2015)
That said, there are people doing it it appears: http://cyrilvoyant.pagesperso-orange.fr/IEE-IC.pdf, so you can surely replicate some of their work somehow in KNIME. No idea how I'd start though, as I've personally pledged for abstention from causal methods a while ago. :)
Sorry, I have only a sliver of an idea, so I'd rather not risk exposing my ignorance. :) Try finding out how authors of other papers have done it!
But again, it appears to be a very niche topic, so you may have to follow these other authors' software choices as well. You can certainly do a whole lot more with KNIME than with many other packages, but this also means that you can treat KNIME much less like a black box. The SPSSs and Statas of this world are more limiting, but can be more forgiving as well. If there's an R script somewhere out there you should be able to "inject" it into KNIME easily for data pre and post processing, but otherwise better go with the flow of the relevant reserach out there.