Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
DOI:
https://doi.org/10.1609/aaai.v37i6.25844Keywords:
ML: Applications, DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data, ML: Causal Learning, ML: Time-Series/Data StreamsAbstract
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc. Real world time series, i.e., large-scale irregular or sparse and intermittent time series, raise significant challenges to existing work attempting to estimate treatment effects. Specifically, the existence of hidden confounders can lead to biased treatment estimates and complicate the causal inference process. In particular, anomaly hidden confounders which exceed the typical range can lead to high variance estimates. Moreover, in continuous time settings with irregular samples, it is challenging to directly handle the dynamics of causality. In this paper, we leverage recent advances in Lipschitz regularization and neural controlled differential equations (CDE) to develop an effective and scalable solution, namely LipCDE, to address the above challenges. LipCDE can directly model the dynamic causal relationships between historical data and outcomes with irregular samples by considering the boundary of hidden confounders given by Lipschitz constrained neural networks. Furthermore, we conduct extensive experiments on both synthetic and real world datasets to demonstrate the effectiveness and scalability of LipCDE.Downloads
Published
2023-06-26
How to Cite
Cao, D., Enouen, J., Wang, Y., Song, X., Meng, C., Niu, H., & Liu, Y. (2023). Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders. Proceedings of the AAAI Conference on Artificial Intelligence, 37(6), 6897-6905. https://doi.org/10.1609/aaai.v37i6.25844
Issue
Section
AAAI Technical Track on Machine Learning I