Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders

TL;DR

This paper introduces LipCDE, a scalable method leveraging Lipschitz regularization and neural controlled differential equations to accurately estimate treatment effects from irregular, large-scale time series data with hidden confounders.

Contribution

The paper proposes LipCDE, a novel approach combining Lipschitz regularization and neural CDEs to handle hidden confounders and irregular sampling in causal time series analysis.

Findings

LipCDE effectively models dynamic causal relationships in irregular time series.

It outperforms existing methods on synthetic and real-world datasets.

LipCDE demonstrates high scalability and robustness in complex scenarios.

Abstract

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.