  Characterizing quantum nonlocality in networks is a challenging, but
important problem. Using quantum sources one can achieve distributions which
are unattainable classically. A key point in investigations is to decide
whether an observed probability distribution can be reproduced using only
classical resources. This causal inference task is challenging even for simple
networks, both analytically and using standard numerical techniques. We propose
to use neural networks as numerical tools to overcome these challenges, by
learning the classical strategies required to reproduce a distribution. As
such, the neural network acts as an oracle, demonstrating that a behavior is
classical if it can be learned. We apply our method to several examples in the
triangle configuration. After demonstrating that the method is consistent with
previously known results, we give solid evidence that the distribution
presented in [N. Gisin, Entropy 21(3), 325 (2019)] is indeed nonlocal as
conjectured. Finally we examine the genuinely nonlocal distribution presented
in [M.-O. Renou et al., PRL 123, 140401 (2019)], and, guided by the findings of
the neural network, conjecture nonlocality in a new range of parameters in
these distributions. The method allows us to get an estimate on the noise
robustness of all examined distributions.
