One of the main challenges in statistical inference is to infer causal relations rather than simple correlations. In this paper , Fabrizio Falasca, Pavel Perezhogin, and Laure Zanna, propose a framework, based on linear response theory, to investigate and diagnose causal mechanisms in the climate system directly from data. The method is physically-based and scales to high-dimensional systems. Possible future applications include the response of the climate system to external perturbations and evaluate climate models outputs.