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Anomaly detection in multivariate timeseries is used in various domains, such as finance, IT, or aerospace, to identify irregular behavior in the used applications. Prior research in anomaly detection has focused on estimating the joint probability of all variables. Then, anomalies are scored based on the probability they receive. Thereby, the variables' dependencies are only considered implicitly. This work follows recent work in anomaly detection that integrates information about the causal relations between the variables in the timeseries into the detection mechanism. The causal mechanisms of the variables are then used to identify anomalies. An observation is identified as anomalous if at least one of the variables it contains deviates from its regular causal mechanism. These regular causal mechanisms are estimated via the conditional distribution of a variable given its causal parent variables, i.e., the variables having a causal influence on a variable. We further develop previous work by gathering information about the causal parents of the variables by applying causal discovery algorithms adapted to the timeseries setting. We apply Conditional Kernel Density Estimation and Conditional Variational Autoencoders to estimate the conditional probabilities. With this causal approach, we outperform methods that rely on the joint probability of the variables in our synthetically generated datasets and the C-MAPPS dataset, which provides simulation data of turbofan engines. Moreover, we investigate the causal approach’s inferred scores on the C-MAPPS dataset to gather insights into the measurements responsible for the prediction of anomalies. Furthermore, we investigate the influence of deviations from the true causal graph on the anomaly detection performance using synthetic data.