| Abstrakt: |
Causal inference seeks to learn the effect of interventions on outcomes. Its potential in the health domain has been dramatically increasing recently, due to advancements in machine learning, as well as in the growing amount of medical data collected. Gaia-X provides a framework to implement Health Data Spaces at scale, in a compliant, secure, and trustable manner. In this paper, we provide a modular online service for causal inference using observational data, building on the Gaia-X framework. While two versions of the IDA algorithm for causal inference are already provided, the service allows users to further contribute algorithms in various programming languages (not only for causal inference), as well as their data, and efficiently execute these on a central server. Additionally, the platform facilitates the exchange of algorithms and data among participants. Users of the platform can enter into agreements with other users over the use of algorithms and data. Calculations can be carried out directly on the platform without the need to locally store foreign data. [ABSTRACT FROM AUTHOR] |
