Estimating treatment effects over time holds significance in various domains,
including precision medicine, epidemiology, economy, and marketing. This paper
introduces a unique approach to counterfactual regression over time,
emphasizing long-term predictions. Distinguishing itself from existing models
like Causal Transformer, our approach highlights the efficacy of employing RNNs
for long-term forecasting, complemented by Contrastive Predictive Coding (CPC)
and Information Maximization (InfoMax). Emphasizing efficiency, we avoid the
need for computationally expensive transformers. Leveraging CPC, our method
captures long-term dependencies in the presence of time-varying confounders.
Notably, recent models have disregarded the importance of invertible
representation, compromising identification assumptions. To remedy this, we
employ the InfoMax principle, maximizing a lower bound of mutual information
between sequence data and its representation. Our method achieves
state-of-the-art counterfactual estimation results using both synthetic and
real-world data, marking the pioneering incorporation of Contrastive Predictive
Encoding in causal inference.

本文提出了一种独特的时间反事实回归方法，通过强调长期预测，强调使用循环神经网络（RNN）进行长期预测，结合对比预测编码（CPC）和信息最大化（InfoMax），避免使用计算昂贵的变换器，捕捉到存在时间变化的混杂因素中的长期依赖关系，通过最大化序列数据和其表示之间相互信息的下界，实现了最先进的反事实估计结果，标志着对比预测编码在因果推断中的先驱性融合。

因果对照学习在时间上的反事实回归

Causal Contrastive Learning for Counterfactual Regression Over Time

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

We address the problem of counterfactual regression using causal inference
(CI) in observational studies consisting of high dimensional covariates and
high cardinality treatments. Confounding bias, which leads to inaccurate
treatment effect estimation, is attributed to covariates that affect both
treatments and outcome. The presence of high-dimensional co-variates
exacerbates the impact of bias as it is harder to isolate and measure the
impact of these confounders. In the presence of high-cardinality treatment
variables, CI is rendered ill-posed due to the increase in the number of
counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural
network (DNN) based framework for estimating causal effects in the presence of
large number of covariates, and high-cardinal and continuous treatment
variables. The proposed architecture comprises of a decorrelation network and
an outcome prediction network. In the decorrelation network, we learn a data
representation in lower dimensions as compared to the original covariates and
addresses confounding bias alongside. Subsequently, in the outcome prediction
network, we learn an embedding of high-cardinality and continuous treatments,
jointly with the data representation. We demonstrate the efficacy of causal
effect prediction of the proposed Hi-CI network using synthetic and real-world
NEWS datasets.

本文提出了一种名为 Hi-CI 的深度神经网络算法，可以使用因果推断 (CI) 从高纬度协变量和高基数处理中进行反事实回归，并通过使用去相关网络和结果预测网络解决混淆偏差问题，最后展示了该算法在合成和真实数据集上的有效性。