The talk examines how astroparticle physics (APP) combines machine learning with causal-mechanistic explanations. To separate data from background in cosmic ray measurements, machine learning is employed. This separation results in probabilities for attributing signals to cosmic sources. APP combines these probabilistic results with causal stories about messenger particles that transfer information from cosmic sources to the Earth, raising philosophical problems regarding the theory-ladenness of data and their interpretation in terms of scientific realism vs. instrumentalism. For clarification, a closer look at the data analysis of APP is taken here, focusing on two questions: How does the machine learning used in the data analysis model the physical data-background-relation? How can probabilistic data give rise to causal stories that explain single signals? As a case study, the neutrino signal measured in 2017 by the IceCube detector in Antarctica serves. I argue that here machine learning was a mere statistical tool, whereas the causal interpretation tracing the signal back to a cosmic source depended crucially on a coincidence with the high-energy gamma ray activity of the blazar TXS 0506+056, indicating the significance of independent evidence and common-cause-arguments in multi-messenger astronomy.