Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?
将非混杂协变量误作混杂变量，会影响潜在结果框架下的因果推断吗？AI 解读 ↓
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将非混杂协变量误作混杂变量，会影响潜在结果框架下的因果推断吗？
- 研究问题
- 在潜在结果框架下，将工具变量、中介、碰撞点等非混杂协变量纳入调整，会如何影响去混杂与反事实结果推断？
- 方法与思路
- 作者提出统一图形框架，分别分析不同类型协变量对推断表现的影响，并通过合成数据实验检验所述理论结论。
- 材料陈述的贡献
- 摘要称，去除混杂偏差时仅纳入混杂变量最优，而后续反事实推断可受益于调整变量，强调两项任务的区别。
- 阅读关注点
- 应核对各类协变量的图形定义、分析覆盖的模型与性能指标，以及“最优”结论成立的条件和实验设计。
- 阅读边界
- 所给来源为 arXiv 预印本，同行评审未经核实；摘要未说明具体图结构、比较条件及结论对不同估计器的适用范围。
原文摘要
The Potential Outcome Framework (POF) plays a prominent role in the field of causal inference. Most causal inference models based on the POF (CIMs-POF) are designed for eliminating confounding bias and default to an underlying assumption of Confounding Covariates. This assumption posits that the covariates consist solely of confounders. However, the assumption of Confounding Covariates is challenging to maintain in practice, particularly when dealing with high-dimensional covariates. While certain methods have been proposed to differentiate the distinct components of covariates prior to conducting causal inference, the consequences of treating non-confounding covariates as confounders remain unclear. This ambiguity poses a potential risk when conducting causal inference in practical scenarios. In this paper, we present a unified graphical framework for the CIMs-POF, which greatly enhances the comprehension of these models' underlying principles. Using this graphical framework, we quantitatively analyze the extent to which the inference performance of CIMs-POF is influenced when incorporating various types of non-confounding covariates, such as instrumental variables, mediators, colliders, and adjustment variables. The key findings are: in the task of eliminating confounding bias, the optimal scenario is for the covariates to exclusively encompass confounders; in the subsequent task of inferring counterfactual outcomes, the adjustment variables contribute to more accurate inferences. Furthermore, extensive experiments conducted on synthetic datasets consistently validate these theoretical conclusions.