A Model Averaging Approach to Causal Inference in Substance Abuse Prevention Research

项目摘要 预防药物滥用研究中因果推断的模型平均方法 许多以学校为基础的预防酒精和其他药物(AOD)使用的评估是 在不遵守规定和辍学的情况下，通过设计或实施来观察。的观测性 对于试图了解干预有效性的研究人员来说，预防研究是一个重大挑战 由于选择的严重威胁和令人困惑的偏见(即，获得更多 干预往往与那些接受较少干预的人非常不同)。此应用程序建议三年R01 研究开发一种使用模型平均的新的因果推理方法，该方法将提供更健壮的 在预防研究中解决这一重大方法学问题的方法不同于目前的方法。 鲁宾因果模型(RCM)是因果推理的一般框架，在这些研究中 随机化是不可能的，或者受到实施困难的影响。虽然经典的统计学 当治疗后和治疗后混杂变量的分布不同时，技术可能会产生严重的偏差 与对照个体相比，RCM可以从有效性估计中减少此类偏差。奈达已经做出了 在区域协调框架下继续开发方法是一项高度优先的研究工作. 目前，实践者的一个主要困难是在众多RCM可用的方法中进行选择。 初步审查表明，已经提出了40多种不同的RCM方法。此外， 数值和实证研究表明，不同方法得出的结论可以有很大的变数，而且 不同的作者推荐了许多不同的方法。因此，最值得推荐的RCM 针对特定应用的方法通常是不确定的。为了应对这一挑战，我们提议开发一部小说 因果推断的模型平均法。当有多个候选估计量时，最优模型 平均估计器在所有候选估计器中提供了最好的统计效率 消除模型选择的敏感性。尽管它们具有关键的优势，但因果关系的模型平均方法 对于选择RCM的问题，文献中还没有彻底的效果调查 接近。我们建议开发因果推理模型平均方法，并开发一个软件工具 来实施新的方法。我们将评估该方法在数值研究中的实用优势，并 在一项评估SELECT有效性的应用研究中，一个突出的以学校为基础的AOD预防 使用。

PROJECT ABSTRACT A Model Averaging Approach to Causal Inference in Substance Abuse Prevention Research Many evaluations of school-based preventions for alcohol and other drugs (AOD) use are either observational by design or by implementation given noncompliance and dropouts. The observational nature of prevention studies is a major challenge to researchers trying to understand an intervention's effectiveness because of the serious threats of selection and confounding biases (i.e., individuals who receive more of the intervention are often very different from those who receive less). This application proposes a three-year R01 study to develop a novel causal inference approach using model averaging that will provide a more robust solution than current approaches to this major methodological problem in prevention research. The Rubin Causal Model (RCM) is a general framework for causal inference with studies in which randomization is not possible or is compromised by implementation difficulties. While classic statistical techniques can be severely biased when the distribution of confounding variables differ between treated and control individuals, the RCM can reduce such biases from effectiveness estimates. NIDA has made the continued development of methods under the RCM framework a high research priority. Currently, a major difficulty for practitioners is to choose among the numerous RCM available approaches. A preliminary review suggests that more than 40 distinct RCM approaches have been proposed. Further, numerical and empirical studies show that the conclusions across methods can be highly variable and that many distinct approaches have been recommended by different authors. Thus, the most recommendable RCM approach for a specific application is often uncertain. To address this challenge, we propose to develop a novel model averaging approach to causal inference. When there are many candidate estimators, the optimal model averaging estimator has been shown to offer the best statistical efficiency among all candidate estimators and eliminate sensitivity from model choice. Despite their key advantages, model averaging methods for causal effects have not been thoroughly investigated in the literature to tackle the issue of choosing an RCM approach. We propose to develop causal inference model averaging methodology and develop a software tool to implement the new method. We will evaluate practical advantages of the method in numerical studies and in an application study evaluating the effectiveness of CHOICE, a prominent school-based prevention for AOD use.