The Estimation of Continual Causal Effect for Dataset Shifting Streams
Journal:
arXiv
Published Date:
Apr 29, 2025
Abstract
Causal effect estimation has been widely used in marketing optimization. The
framework of an uplift model followed by a constrained optimization algorithm
is popular in practice. To enhance performance in the online environment, the
framework needs to be improved to address the complexities caused by temporal
dataset shift. This paper focuses on capturing the dataset shift from user
behavior and domain distribution changing over time. We propose an Incremental
Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle
this challenge. The ICE-PKD framework includes two components: (i) a
multi-treatment uplift network that eliminates confounding bias using
counterfactual regression; (ii) an incremental training strategy that adapts to
the temporal dataset shift by updating with the latest data and protects
generalization via replay-based knowledge distillation. We also revisit the
uplift modeling metrics and introduce a novel metric for more precise online
evaluation in multiple treatment scenarios. Extensive experiments on both
simulated and online datasets show that the proposed framework achieves better
performance. The ICE-PKD framework has been deployed in the marketing system of
Huaxiaozhu, a ride-hailing platform in China.
