CGFLEX: A Flexible Framework for Causal Graph-based Data Synthesis
| Vortragende(r) | Paul Giza | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Bela Böhnke | |
| Termin | Fr 14. April 2023 | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | Algorithms that extract dependencies from data and represent them as causal graphs must also be tested. For such tests, data with a known ground truth is required, but this is rarely available. Generating data under controlled conditions through simulations is expensive and time-consuming. A solution to this problem is to create synthetic datasets, where dependencies are predefined, to evaluate the results of these algorithms.
This work focuses on building a framework for the synthesis of data. In the framework, the synthesis process starts with generating a random dependency graph, specifically a directed acyclic graph. In this graph, every node, except the source nodes, has parent nodes. The nodes represent variables. In the next step, we populate each node with predefined random dependencies. A dependency is a model that defines the value of a variable based on its parent variables. From this structure, datasets can be sampled. Users have control over the properties of the causal graph through various parameters and can choose from multiple types of dependencies, representing different complexity levels. Additionally, the sampling process allows interactivity through parameter settings, providing a tool for improving and comparing the aforementioned algorithms under various conditions. | |