When studying treatment effects in multilevel studies, investigators commonly use (semi-)parametric estimators, which make strong parametric assumptions about the outcome, the treatment, and/or the correlation structure between study units in a cluster. We propose a novel estimator of treatment effects that does not make such assumptions. Specifically, the new estimator is shown to be doubly robust, asymptotically Normal, and often more efficient than existing estimators, all without having to make any parametric modeling assumptions about the outcome, the treatment, and the correlation structure. We achieve this by estimating two non-standard nuisance functions in causal inference, the conditional propensity score and the outcome covariance model, using existing existing machine learning methods designed for independent and identically distributed (i.i.d) data. The new estimator is also demonstrated in simulated and real data where the new estimator is drastically more efficient than existing estimators, especially when studying cluster-specific treatment effects.
翻译:在多层次研究中研究治疗效果时,调查人员通常使用(半)参数估计器,对结果、处理和(或)一组研究单元之间的相关性结构作出强烈的参数假设。我们建议对治疗效果提出新的估计器,但并不作这种假设。具体地说,新的估计器显示具有双重强健性,无常态性,往往比现有的估计器更有效率,而不必对结果、治疗和相关性结构作任何参数模型假设。我们通过估计因果关系推断中的两种非标准干扰功能、有条件偏差分和结果共变模型,利用现有为独立和相同分布(i.d.)数据设计的机器学习方法。新的估计器还表现在模拟和实际数据中,新估计器比现有估计器效率高得多,特别是在研究特定类治疗效果时。