While the knowledge about the structures of a system’s underlying causal relationships is crucial within many real-world scenarios, the omnipresence of heterogeneous data characteristics impedes applying methods for causal structure learning (CSL).
In this dissertation project, we reduce the barriers for the transfer of CSL into practice with threefold contributions:
(1) We derive an information-theoretic conditional independence test that, incorporated into methods for CSL, improves the accuracy for non-linear and mixed discrete-continuous causal relationships;
(2) We develop a modular pipeline that covers the essential components required for a comprehensive benchmarking to support the transferability into practice;
(3) We evaluate opportunities and challenges of CSL within different real-world scenarios from genetics and discrete manufacturing to demonstrate the accuracy of our approach in practice.
cuPC: CUDA-Based Parallel PC Algorithm for Causal Structure Learning on GPU
