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关键作用。在观察性数据中进行去混杂与反事实预测已成为因果推断研究的重要课题。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一项重大挑战，它扭曲因果推断结果并影响反事实推断的准确性。为此，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），该模型能够生成未观测混杂因子的后验分布。VLUCI放宽了多数因果推断方法常忽视的无混杂假设。通过解耦已观测与未观测混杂因子，VLUCI构建了双重变分推断模型来近似未观测混杂因子的分布，进而用于推断更准确的反事实结果。在合成数据集与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具有优越性能。该模型与当前最先进的反事实推断模型兼容，可在群体层面和个体层面显著提升推断准确性。此外，VLUCI提供反事实结果的置信区间，有助于风险敏感领域的决策制定。我们进一步以公开的IHDP数据集为例，阐明了将VLUCI应用于未观测混杂因子不完全符合模型假设的场景时的考量因素，突显了VLUCI的实用优势。