Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confounder affected by exposure. We present a theoretical and computational study of the properties of the interventional (in)direct effect estimands based on the efficient influence function in the nonparametric statistical model. We use the efficient influence function to develop two asymptotically optimal nonparametric estimators that leverage data-adaptive regression for the estimation of nuisance parameters: a one-step estimator and a targeted minimum loss estimator. We further present results establishing the conditions under which these estimators are consistent, multiply robust, |$n^{1/2}$|-consistent and efficient. We illustrate the finite-sample performance of the estimators and corroborate our theoretical results in a simulation study. We also demonstrate the use of the estimators in our motivating application to elucidate the mechanisms behind the unintended harmful effects that a housing intervention had on risky behaviour in adolescent girls.

中文翻译：

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具有中间混杂因素的非参数有效因果中介

中介分析的干预效应被提议作为在存在受暴露影响的中介结果混杂因素的情况下缺乏自然（非）直接效应的可识别性的解决方案。我们基于非参数统计模型中的有效影响函数，对介入（in）直接效应估计的属性进行了理论和计算研究。我们使用有效的影响函数来开发两个渐近最优的非参数估计器，它们利用数据自适应回归来估计干扰参数：一步估计器和目标最小损失估计器。我们进一步展示了建立这些估计量一致、乘法稳健的条件的结果，|$n^{1/2}$|- 一致和高效。我们说明了估计器的有限样本性能，并在模拟研究中证实了我们的理论结果。我们还展示了在我们的激励应用程序中使用估计量来阐明住房干预对少女危险行为产生的意外有害影响背后的机制。