In many scientific disciplines, coarse-grained causal models are used to explain and predict the dynamics of more fine-grained systems. Naturally, such models require appropriate macrovariables. Automated procedures to detect suitable variables would be useful to leverage increasingly available high-dimensional observational datasets. This work introduces a novel algorithmic approach that is inspired by a new characterisation of causal macrovariables as information bottlenecks between microstates. Its general form can be adapted to address individual needs of different scientific goals. After a further transformation step, the causal relationships between learned variables can be investigated through additive noise models. Experiments on both simulated data and on a real climate dataset are reported. In a synthetic dataset, the algorithm robustly detects the ground-truth variables and correctly infers the causal relationships between them. In a real climate dataset, the algorithm robustly detects two variables that correspond to the two known variations of the El Nino phenomenon.