Inferring the presence of causal links from observational data is a challenging task which typically goes under the name of causal discovery. After reviewing the basic concepts of this field and a standard algorithm used to infer the topology of causal graphs, we develop an approach specifically designed for times series data based on the Information Imbalance measure. This estimator, first introduced by Glielmo et al., 2022 for ranking the information content of different distance spaces, has been applied by Del Tatto et al., 2024 to detect causal relationships between time-dependent variables. Recently, it has been reformulated in a differentiable version (Wild et al., 2024) which allows the automatic learning of the most informative distance function in a gradient-descent fashion.