Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial
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映射至目标试验的干预效应：高维中介分析中的因果机器学习
- 研究问题
- 当中介变量维度较高时，如何估计由目标试验定义、对应联合中介分布不同改变方式的干预效应，以评估假想中介干预可能缓解多少暴露相关风险？
- 方法与思路
- 作者针对三种目标试验定义的干预效应，基于有效影响函数构造一步估计与部分靶向最小损失估计，并用澳大利亚儿童纵向研究的代谢指标案例说明。
- 材料陈述的贡献
- 摘要称，新估计量弥补既有单重稳健参数方法在高维应用中的限制，并在一定条件下具有根号样本量一致性、有效性和多重稳健性。
- 阅读关注点
- 应核对三个目标试验的干预规则与联合中介分布变换、各效应的识别条件，以及影响函数、干扰参数收敛率和多重稳健性的具体模型组合。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未列效率与稳健性所需完整条件；假想中介干预的效应也不能直接等同于可实施政策的效果。
原文摘要
Causal mediation analysis examines causal pathways linking exposures to disease. The estimation of interventional effects, which are mediation estimands that overcome certain identifiability problems of natural effects, has been advanced through causal machine learning methods, particularly for high-dimensional mediators. Recently, it has been proposed interventional effects can be defined in each study by mapping to a target trial assessing specific hypothetical mediator interventions. This provides an appealing framework to directly address real-world research questions about the extent to which such interventions might mitigate an increased disease risk in the exposed. However, existing estimators for interventional effects mapped to a target trial rely on singly-robust parametric approaches, limiting their applicability in high-dimensional settings. Building upon recent developments in causal machine learning for interventional effects, we address this gap by developing causal machine learning estimators for three interventional effect estimands, defined by target trials assessing hypothetical interventions inducing distinct shifts in joint mediator distributions. These estimands are motivated by a case study within the Longitudinal Study of Australian Children, used for illustration, which assessed how intervening on high inflammatory burden and other non-inflammatory adverse metabolomic markers might mitigate the adverse causal effect of overweight or obesity on high blood pressure in adolescence. We develop one-step and (partial) targeted minimum loss-based estimators based on efficient influence functions of those estimands, demonstrating they are root-n consistent, efficient, and multiply robust under certain conditions.