Unveiling Causal Mediation Pathways in High-Dimensional Mixed Exposures: A Data-Adaptive Target Parameter Strategy
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高维混合暴露的因果中介路径识别：数据自适应目标参数策略
- 研究问题
- 面对多种连续、二元或类别暴露与中介变量，如何发现未知的暴露—中介路径，并估计针对这些路径干预的直接和间接效应？
- 方法与思路
- NOVAPathways 在参数生成折中发现路径，在估计折中结合随机干预、序贯半参数回归和机器学习，估计数据自适应目标参数。
- 材料陈述的贡献
- 摘要称估计量在干扰函数达到 n⁻¹/⁴ 一致性等条件下渐近线性；模拟中量化暴露支持根号 n 一致性，连续暴露受数值积分限制。
- 阅读关注点
- 应核对数据自适应目标与自然直接、间接效应的对应关系、随机干预识别条件，以及样本分折和积分误差如何进入推断。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未列全识别及速率条件，且明确指出真正连续数据的积分近似阻碍根号 n 一致性。
原文摘要
Mediation analysis in causal inference typically concentrates on one binary exposure, using deterministic interventions to split the average treatment effect into direct and indirect effects through a single mediator. Yet, real-world exposure scenarios often involve multiple continuous exposures impacting health outcomes through varied mediation pathways, which remain unknown a priori. Addressing this complexity, we introduce NOVAPathways, a methodological framework that identifies exposure-mediation pathways and yields unbiased estimates of direct and indirect effects when intervening on these pathways. By pairing data-adaptive target parameters with stochastic interventions, we offer a semi-parametric approach for estimating causal effects in the context of high-dimensional, continuous, binary, and categorical exposures and mediators. In our proposed cross-validation procedure, we apply sequential semi-parametric regressions to a parameter-generating fold of the data, discovering exposure-mediation pathways. We then use stochastic interventions on these pathways in an estimation fold of the data to construct efficient estimators of natural direct and indirect effects using flexible machine learning techniques. Our estimator proves to be asymptotically linear under conditions necessitating n to the negative quarter consistency of nuisance function estimation. Simulation studies demonstrate the square root n consistency of our estimator when the exposure is quantized, whereas for truly continuous data, approximations in numerical integration prevent square root n consistency. Our NOVAPathways framework, part of the open-source SuperNOVA package in R, makes our proposed methodology for high-dimensional mediation analysis available to researchers, paving the way for the application of modified exposure policies which can delivery more informative statistical results for public policy.