In this work, we focus on the problem of answering probabilistic queries in the context of continuous-time event data, focusing in particular on neural autoregressive models. Probabilistic queries have potentially broad applications and can include queries such as “the probability of an event of type A occurring before type B” or “the probability of at least one event of type A occurring before time T”. Given that computation of such query probabilities is usually intractable and lacks analytical forms in general, we propose a general theoretical framework and a novel marginalization scheme that enables us to leverage importance sampling to answer these queries in a computationally efficient manner for any black-box autoregressive model. We evaluate our approach by presenting results with multiple real-world continuous-time event datasets, and demonstrate that our approach can be significantly more computationally efficient than the naive estimation. Please refer to the PDF for the one-page extended abstract for additional details.