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.

本文介绍了一种基于 Lipschitz regularization 和神经控制微分方程（CDE）的方法，名为 LipCDE，用于解决时序数据的因果分析问题，该方法能够准确估算个体化的治疗效果和克服隐藏混淆变量导致的治疗估计偏差和高方差情况，并通过实验验证了方法的有效性和可扩展性。

利用隐藏变量从不规则时间序列观测中估计治疗效果

Estimating Treatment Effects from Irregular Time Series Observations  with Hidden Confounders

We consider the problem of modeling the dynamics of continuous
spatial-temporal processes represented by irregular samples through both space
and time. Such processes occur in sensor networks, citizen science, multi-robot
systems, and many others. We propose a new deep model that is able to directly
learn and predict over this irregularly sampled data, without voxelization, by
leveraging a recent convolutional architecture for static point clouds. The
model also easily incorporates the notion of multiple entities in the process.
In particular, the model can flexibly answer prediction queries about arbitrary
space-time points for different entities regardless of the distribution of the
training or test-time data. We present experiments on real-world weather
station data and battles between large armies in StarCraft II. The results
demonstrate the model's flexibility in answering a variety of query types and
demonstrate improved performance and efficiency compared to state-of-the-art
baselines.

本论文提出了一种使用深度学习模型对不规则采样的连续空间时间过程进行直接学习和预测的方法，并验证了该模型在真实天气数据和星际争霸 II 中的有效性。