  Modeling and analysis for event series generated by heterogeneous users of
various behavioral patterns are closely involved in our daily lives, including
credit card fraud detection, online platform user recommendation, and social
network analysis. The most commonly adopted approach to this task is to
classify users into behavior-based categories and analyze each of them
separately. However, this approach requires extensive data to fully understand
user behavior, presenting challenges in modeling newcomers without historical
knowledge. In this paper, we propose a novel discrete event prediction
framework for new users through the lens of causal inference. Our method offers
an unbiased prediction for new users without needing to know their categories.
We treat the user event history as the ''treatment'' for future events and the
user category as the key confounder. Thus, the prediction problem can be framed
as counterfactual outcome estimation, with the new user model trained on an
adjusted dataset where each event is re-weighted by its inverse propensity
score. We demonstrate the superior performance of the proposed framework with a
numerical simulation study and two real-world applications, including Netflix
rating prediction and seller contact prediction for customer support at Amazon.
