Beyond undirected correlations that can be detected by statistical analysis or learned by typical machine learning aproaches based on data provided causality analysis might provide further insights into confounders and root cause of systematic behaviour.
Our services are able to efficiently analyze large data sets for causalities. We specialize in time series analysis, but can also analyze causalities in heterogeneous data sources, e.g., with natural language or categorical data. For these services, we can use HTC and HPC infrastructure for Big Data analysis.
We are able to analyze the validity of certain approaches using simulated data sources. Our benchmarking efforts can also produce clear performance measures to demonstrate the effectiveness of algorithmic causality analysis.
Together with leading industry partners, we have successfully applied causality analysis to industrial applications (e.g., root cause analysis) or financial applications.
The tasks are performed by experienced ML researchers at KIT. KIT is "The Research University in the Helmholtz Association". As one of the largest scientific institutions in Europe, the only German university of excellence with national large-scale research facilities combines a long university tradition with program-oriented cutting-edge research. Since KIT also has a focus on innovation and technology transfer, our experts have many years of experience from applied industrial projects.
SPECIAL ACCESS CONDITIONS
Conditions and requirements for participation in an experiment within the Open Calls:
By participating in an EUHubs4Data Open Call, you are initially only applying for funding that originally comes from the European Commission and is awarded by the coordinator exclusively in its own name under the conclusion of a sub-grant agreement. This sub-grant agreement does not establish a contract with KIT, neither through your application nor through a possible positive funding decision.
KIT will therefore - also in your own interest - conclude a separate written agreement with you at the start of the experiment (based on our sample cooperation agreement: https://www.sdil.de/en/euhubs4data/sdil-model-cooperation-agreement). If you decide to propose the participation of KIT and SDIL infrastructure in your experiment, you must respect the following conditions: https://www.sdil.de/en/euhubs4data/sdil-terms. We provide this information in advance to ensure maximum transparency: please contact us if you have any questions. In the unlikely event that you are unable to conduct your experiment with our participation, we will attempt to assist you in selecting alternative services before the experiment begins.
Please note that contrary to the name "service", the above description is not a genuine commercial offer, but a listing of exclusive contributions as part of a genuine eye-to-eye collaboration.
For genuine commercial offerings related to the above topics, please feel free to contact us any time outside of the Open Calls.
Data set(s) with at least annecdotal evidence of causal relations, ideally but not strictly neccessary aproaches for data simulation
We recently implemented multiple aproaches of causality analysis to explore future aproaches to ML-supported risk monitoring at investment branch of the Deutsch Bank: