New User Event Prediction Through the Lens of Causal Inference

TL;DR

This paper introduces a causal inference-based framework for predicting user events, especially for new users with limited history, by treating user history as treatment and estimating counterfactual outcomes, improving prediction accuracy.

Contribution

It presents a novel discrete event prediction method that does not require user categorization or extensive historical data, using inverse propensity scoring for counterfactual estimation.

Findings

Enhanced prediction accuracy demonstrated in simulations.

Improved Netflix rating prediction performance.

Better seller contact prediction at Amazon.

Abstract

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.