Modeling Uplift from Observational Time-Series in Continual Scenarios

Sanghyun Kim, Jungwon Choi, NamHee Kim, Jaesung Ryu, Juho Lee

Keywords: uplift modeling, time-series, data mining, continual learning, causal inference

TL;DR: We release Backend-TS dataset for uplift modeling in continual learning scenarios.

Abstract: As the importance of causality in machine learning grows, we expect the model to learn the correct causal mechanism for robustness even under distribution shifts. Since most of the prior benchmarks focused on vision and language tasks, domain or temporal shifts in causal inference tasks have not been well explored. To this end, we introduce Backend-TS dataset for modeling uplift in continual learning scenarios. We build the dataset with CRUD data and propose continual learning tasks under temporal and domain shifts.