New User Event Prediction Through the Lens of Causal Inference
This addresses the challenge of modeling newcomers without extensive historical data in domains like recommendation systems and fraud detection, though it appears incremental by applying causal methods to a specific bottleneck.
The paper tackles the problem of predicting events for new users with limited history by framing it as a counterfactual outcome estimation problem using causal inference, treating user history as treatment and category as a confounder. It demonstrates improved performance in numerical simulations and real-world applications like Netflix rating prediction and Amazon seller contact prediction.
Modeling and analysis for event series generated by users of heterogeneous 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 assign users to behavior-based categories and analyze each of them separately. However, this requires extensive data to fully understand the user behavior, presenting challenges in modeling newcomers without significant historical knowledge. In this work, we propose a novel discrete event prediction framework for new users with limited history, without needing to know the user's category. 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, where each event is re-weighted by its inverse propensity score. We demonstrate the improved 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.