VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference
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VLUCI：用于反事实推断的未观测混杂因素变分学习
- 研究问题
- 存在未观测混杂时，能否通过潜变量学习改善群体与个体反事实结果推断，并提供反事实结果的不确定性区间？
- 方法与思路
- 作者提出双重变分推断模型，分离已观测与未观测混杂并近似后者的后验分布，再与反事实推断模型结合。
- 材料陈述的贡献
- 摘要称，合成及半合成实验显示反事实推断准确性改善，并提供结果区间及基于 IHDP 数据的模型假设偏离讨论。
- 阅读关注点
- 应核对未观测混杂的识别信息、潜变量模型假设、后验近似与因果识别的关系，以及结果区间的校准方式。
- 阅读边界
- 所给来源为 arXiv 预印本，同行评审未经核实；摘要未说明识别条件与覆盖率，学习潜在混杂本身不足以验证因果识别。
原文摘要
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.