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

项目摘要 药物滥用预防研究中因果推理的模型平均法 许多对学校预防酒精和其他药物使用（AOD）的评价是 通过设计或实施观察不依从和辍学。的观察性质 预防研究是研究人员试图了解干预措施有效性的主要挑战 由于选择和混杂偏差的严重威胁（即，个人谁收到更多的 干预措施往往与那些得到较少干预措施的国家大不相同）。本申请提出了一项为期三年的R01 研究开发一种新的因果推理方法，使用模型平均值， 解决这一预防研究中的主要方法问题。 鲁宾因果模型（Rubin Causal Model，简称RCM）是一个通用的因果推理框架， 随机化是不可能的，或者由于实施困难而受到损害。虽然经典的统计 当治疗组和对照组之间混杂变量的分布不同时， 控制个人，RCM可以减少这种偏差的有效性估计。NIDA已经使 在区域协调机制框架下继续发展方法是一个高度优先的研究事项。 目前，从业人员的一个主要困难是在众多的RCM可用方法中进行选择。 初步审查表明，已经提出了40多种不同的区域协调机制方法。此外，本发明还 数值和实证研究表明，不同方法得出的结论差异很大， 不同的作者推荐了许多不同的方法。因此，最具竞争力的RCM 具体应用的方法往往是不确定的。为了应对这一挑战，我们建议开发一种新的 模型平均法的因果推理。当有许多候选估计量时， 平均估计量已被证明在所有候选估计量中提供最好的统计效率， 消除模型选择的敏感性。尽管它们具有关键优势，但因果关系的模型平均方法 在文献中还没有彻底研究的影响，以解决选择一个RCM的问题 approach.我们建议发展因果推理模型平均方法，并开发一个软件工具 来实施新方法。我们将评估该方法在数值研究中的实际优势， 在一项应用研究中，评估了“选择”计划的成效。“选择”计划是一项以学校为基础的预防急性呼吸道感染的主要计划， 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.