Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
This work addresses a key challenge in causal inference for real-world applications like finance and healthcare, where irregular time series and hidden confounders complicate treatment effect estimation, representing an incremental improvement by combining existing techniques to handle specific bottlenecks.
The paper tackles the problem of estimating individualized treatment effects from irregular time series data with hidden confounders, which can cause biased and high-variance estimates, by proposing LipCDE, a method that leverages Lipschitz regularization and neural controlled differential equations to model dynamic causal relationships, and demonstrates its effectiveness and scalability through experiments on synthetic and real-world datasets.
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 can include large-scale, irregular, and intermittent time series observations, raising 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.