AI 中文总结
研究针对因果中介分析中常被忽视的正性假设，将PoRT算法扩展到多种中介效应，通过心理健康应用展示其用法，还讨论了正性假设违背的后果及建议，为因果中介分析提供了有效检验方法。
AI 中文摘要
因果中介分析在心理科学中应用日益广泛。在所需假设中，正性假设因缺乏检验工具而常被忽视。中介正性比标准非中介暴露效应分析中的正性更复杂，因其对暴露和中介都要求正性，且正性假设的具体形式取决于感兴趣的中介估计量。我们将最近设计用于在非中介环境中检验正性且无需对建模或数据生成过程做假设的正性回归树（PoRT）算法扩展到受控、自然和干预性中介效应。通过心理健康应用进行说明，并通过R包和相关笔记本提供。最后讨论了正性假设违背的后果及建议。
英文摘要
Causal mediation analyses are increasingly used in psychological sciences. Among the required assumptions, positivity is unfortunately seldom mentioned, likely due to the lack of tools for checking it. Mediational positivity is more complex than positivity in standard, non-mediated exposure effect analysis, because it requires positivity for both the exposure and the mediator, and because the specific form of the positivity assumption depends on the mediation estimand of interest. We propose an extension of the Positivity Regression Trees (PoRT) algorithm -- which was recently designed to check positivity in non-mediated settings without requiring assumptions about the modeling or the data-generating process -- to controlled, natural and interventional mediational effects. We illustrate its use through an application in mental health and have made it accessible through the port R package and a related notebook available at github.com/ArthurChatton/dePoRT-notebook. Finally, we discuss consequences and provide recommendations for when positivity violations are identified.
