  We present principled Bayesian model comparison through simulation-based
neural classification applied to SN Ia analysis. We validate our approach on
realistically simulated SN Ia light curve data, demonstrating its ability to
recover posterior model probabilities while marginalizing over >4000 latent
variables. The amortized nature of our technique allows us to explore the
dependence of Bayes factors on the true parameters of simulated data,
demonstrating Occam's razor for nested models. When applied to a sample of 86
low-redshift SNae Ia from the Carnegie Supernova Project, our method prefers a
model with a single dust law and no magnitude step with host mass, disfavouring
different dust laws for low- and high-mass hosts with odds in excess of 100:1.
