In machine learning, likelihood-free inference refers to the task of performing such analysis driven by data instead of an analytical expression. We discuss the application of neural spline flows, a neural density estimation algorithm, to the likelihood-free inference problem of the measurement of neutrino oscillation parameters in long baseline neutrino experiments. A method adapted to physics parameter inference is developed and applied to the case of the disappearance muon neutrino analysis at the T2K experiment.