As a promising individualized treatment effect (ITE) estimation method,
counterfactual regression (CFR) maps individuals' covariates to a latent space
and predicts their counterfactual outcomes. However, the selection bias between
control and treatment groups often imbalances the two groups' latent
distributions and negatively impacts this method's performance. In this study,
we revisit counterfactual regression through the lens of information bottleneck
and propose a novel learning paradigm called Gromov-Wasserstein information
bottleneck (GWIB). In this paradigm, we learn CFR by maximizing the mutual
information between covariates' latent representations and outcomes while
penalizing the kernelized mutual information between the latent representations
and the covariates. We demonstrate that the upper bound of the penalty term can
be implemented as a new regularizer consisting of $i)$ the fused
Gromov-Wasserstein distance between the latent representations of different
groups and $ii)$ the gap between the transport cost generated by the model and
the cross-group Gromov-Wasserstein distance between the latent representations
and the covariates. GWIB effectively learns the CFR model through alternating
optimization, suppressing selection bias while avoiding trivial latent
distributions. Experiments on ITE estimation tasks show that GWIB consistently
outperforms state-of-the-art CFR methods. To promote the research community, we
release our project at this https URL

通过信息瓶颈的视角，我们提出了一种新的学习范式 ——Gromov-Wasserstein 信息瓶颈（GWIB），通过最大化协变量的潜在表示和结果之间的互信息，同时对潜在表示和协变量之间的核互信息进行惩罚，实现了对个体化治疗效应估计模型的学习和抑制选择偏倚的效果。实验证明，GWIB 在个体化治疗效应估计任务中优于现有的 CFR 方法。

透过 Gromov-Wasserstein 信息瓶颈重新审视反事实回归

Revisiting Counterfactual Regression through the Lens of  Gromov-Wasserstein Information Bottleneck

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。