CGFLEX: A Flexible Framework for Causal Graph-based Data Synthesis
| Vortragende(r) | Paul Giza | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Bela Böhnke | |
| Termin | Fr 14. April 2023 | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | Algorithms that can extract dependencies from data and represent them as causal graphs must also be tested. Often, only very few data is available for this and simulations are expensive and time-consuming. Another problem is that even when data is available, the ground truth about the underlying dependencies is usually not known. One solution to this problem is to generate synthetic datasets and use them to evaluate the results of said algorithms.
This work is concerned with building a framework for the synthesis of data. The synthesis process within such a framework would be to first generate a random dependency graph, and then in a second step populate this graph with random dependencies. From this construct, data sets could then be sampled. Furthermore, the user should be able to influence the size and structure of the dependency graph by controlling input values. And by defining the types of dependencies, it is possible to influence the complexity of the graph. Thus one receives an instrument for improvement and comparison of mentioned algorithms under various circumstances. | |