Semiconductor lithography system issues are challenging to diagnose from predefined models and historic data alone. That’s because such systems are characterized by high-dimensionality, non-stationarity and nonlinear behavior across multiple time and spatial scales. One line of research at ASML is to investigate model-independent approaches, which can help to find previously unknown causal relations from system diagnostic data. Therefore, we combine information theory based transfer entropy with eigencentrality in time series analysis. However, this approach must be computationally efficient for ASML system diagnostics (and prognostics) in real time. Moreover, it must be able to locate unobserved variables and capture unique as well as joint causal influences. This presentation will discuss the merits and challenges of causal inference using information-theoretical concepts within ASML context and demonstrate a number of cases.