Interaction Testing in Variation Analysis
变异分析中的交互作用检验AI 解读 ↓
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变异分析中的交互作用检验
- 研究问题
- 如何解释观察状态下处理与结果之间的关联，将条件均值差分解为直接、间接和混杂成分，并判断这些路径是否交互？
- 方法与思路
- 作者扩展中介分析，以两组条件均值之差作为总变异目标，将其分解为因果路径、混杂路径及交互项，并检验交互项。
- 材料陈述的贡献
- 摘要称，该框架可解释自然观察状态下的关联，并通过交互检验判断是否可以采用更简约的总变异分解。
- 阅读关注点
- 应核对各路径和交互项的反事实定义、识别条件及检验构造，特别区分观察关联分解与平均处理效应分解的解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明识别和检验条件，不显著结果也不能直接证明交互为零。
原文摘要
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.