提出新方法分解变量间关联的因果与混杂效应，支持观测数据下的机制解释。
Interaction Testing in Variation Analysis
- 基于总变异（TV）度量扩展中介分析，同时拆解直接、间接和混杂路径。
- 首次引入交互项检验，判断不同作用路径是否存在显著交互效应。
- 适用于自然场景下解释变量关联原因，适合社会科学与医学研究者使用。
因果关系在解释科学现象中至关重要。研究者不仅关注因果效应，还希望理解原因X如何通过特定机制影响结果Y——即揭示从X到Y的因果路径。传统中介分析聚焦于平均处理效应（ATE）的分解，仅涵盖因果路径，适用于随机干预情形。然而，更多情况下需解释观察数据中的关联性。本文提出「变异分析」，以总变异（TV）度量 $\mathrm{E}[Y \mid X=x_1] - \mathrm{E}[Y \mid X=x_0]$ 为核心，涵盖因果与混杂效应。该度量适用于自然状态下的解释，回答“为何X与Y有关”问题。我们进一步将TV分解为直接、间接与混杂效应，并引入路径间的交互项。随后提出交互检验，通过假设检验判断交互项是否显著非零；若不显著，则可采用更简洁的分解形式。
原文摘要 · Abstract (English)
Relationships of cause and effect are of prime importance for explaining scientific phenomena. Often, rather than just understanding the effects of causes, researchers also wish to understand how a cause $X$ affects an outcome $Y$ mechanistically -- i.e., what are the causal pathways that are activated between $X$ and $Y$. For analyzing such questions, a range of methods has been developed over decades under the rubric of causal mediation analysis. Traditional mediation analysis focuses on decomposing the average treatment effect (ATE) into direct and indirect effects, and therefore focuses on the ATE as the central quantity. This corresponds to providing explanations for associations in the interventional regime, such as when the treatment $X$ is randomized. Commonly, however, it is of interest to explain associations in the observational regime, and not just in the interventional regime. In this paper, we introduce \text{variation analysis}, an extension of mediation analysis that focuses on the total variation (TV) measure between $X$ and $Y$, written as $\mathrm{E}[Y \mid X=x_1] - \mathrm{E}[Y \mid X=x_0]$. The TV measure encompasses both causal and confounded effects, as opposed to the ATE which only encompasses causal (direct and mediated) variations. In this way, the TV measure is suitable for providing explanations in the natural regime and answering questions such as ``why is $X$ associated with $Y$?''. Our focus is on decomposing the TV measure, in a way that explicitly includes direct, indirect, and confounded variations. Furthermore, we also decompose the TV measure to include interaction terms between these different pathways. Subsequently, interaction testing is introduced, involving hypothesis tests to determine if interaction terms are significantly different from zero. If interactions are not significant, more parsimonious decompositions of the TV measure can be used.
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