Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing models tackle observed confounders, the presence of unobserved confounders remains a significant challenge, distorting causal inference and impacting counterfactual outcome accuracy. To address this, we propose a novel variational learning model of unobserved confounders for counterfactual inference (VLUCI), which generates the posterior distribution of unobserved confounders. VLUCI relaxes the unconfoundedness assumption often overlooked by most causal inference methods. By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes. Extensive experiments on synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance in inferring unobserved confounders. It is compatible with state-of-the-art counterfactual inference models, significantly improving inference accuracy at both group and individual levels. Additionally, VLUCI provides confidence intervals for counterfactual outcomes, aiding decision-making in risk-sensitive domains. We further clarify the considerations when applying VLUCI to cases where unobserved confounders don't strictly conform to our model assumptions using the public IHDP dataset as an example, highlighting the practical advantages of VLUCI.

翻译：因果推断在流行病学、医疗保健和经济学等多个领域发挥着至关重要的作用。在观测数据中进行去混杂和反事实预测已成为因果推断研究中的核心问题。现有模型虽能处理已观测混杂因子，但未观测混杂因子的存在仍是重大挑战，它会扭曲因果推断并影响反事实结果的准确性。为解决此问题，我们提出了一种用于反事实推断的未观测混杂因子变分学习模型（VLUCI），该模型生成未观测混杂因子的后验分布。VLUCI放宽了大多数因果推断方法常忽视的未混杂性假设。通过分离已观测与未观测混杂因子，VLUCI构建了一个双重变分推断模型来逼近未观测混杂因子的分布，并将其用于推断更精确的反事实结果。在合成数据集和半合成数据集上的大量实验表明，VLUCI在推断未观测混杂因子方面表现出优越性能。该模型与现有最先进的反事实推断方法兼容，显著提升了群体和个体层面的推断精度。此外，VLUCI可为反事实结果提供置信区间，有助于风险敏感领域的决策制定。我们以公开IHDP数据集为例，进一步阐明了将VLUCI应用于未观测混杂因子不完全符合模型假设场景时的注意事项，凸显了VLUCI的实际优势。