Causal Inference from Two-level Factorial Designs

研究人员开发了一个框架，从两个层次的析因和部分析因设计的因果推理，特别敏感的应用到社会，行为和生物医学科学。该框架利用的概念，潜在的结果，在于在中心阶段的因果推理和扩展奈曼的重复抽样方法的因果效应和随机化测试的基础上，费舍尔的尖锐零假设的情况下，2水平析因实验的估计。该框架允许从一个有限的人口统计推断，允许定义和估计参数以外的“平均因子效应”，并导致更灵活的推理程序比那些基于普通的最小二乘估计从线性模型。当调查由于随机化限制而成为观察性研究而不是随机析因实验时，它也确保了统计推断的有效性。析因设计允许有效和成本效益地评估几个因素的相对影响及其对感兴趣的输出变量的相互作用。这样的设计已经成功地应用于几个科学，工程和工业的努力，但不经常使用在社会，行为或生物医学科学，尽管在这些领域的几个潜在的应用。所提出的方法解决了上述领域中与多因素实验相关的复杂性，并具有广泛的应用。例如，它可以应用于评估几项新举措对高中教育的影响;或进行具有成本效益的临床试验，以研究为患有某种疾病的患者提供的不同治疗的单独和综合效果;或确定影响材料科学中复杂物理过程产量的关键因素，如纳米结构的合成。它也可以应用于比较有效性研究（例如，循证医学）。

The investigators develop a framework for causal inference from two-level factorial and fractional factorial designs with particular sensitivity to applications to social, behavioral and biomedical sciences. The framework utilizes the concept of potential outcomes that lies at the center stage of causal inference and extends Neyman's repeated sampling approach for estimation of causal effects and randomization tests based on Fisher's sharp null hypothesis to the case of 2-level factorial experiments. The framework allows for statistical inference from a finite population, permits definition and estimation of parameters other than ``average factorial effects'' and leads to more flexible inference procedures than those based on ordinary least squares estimation from a linear model. It also ensures validity of statistical inference when the investigation becomes an observational study in lieu of a randomized factorial experiment due to randomization restrictions.Factorial designs allow efficient and cost-effective assessments of the relative effects of several factors and their interactions on output variables of interest. Such designs have been successfully applied in several scientific, engineering and industrial endeavors, but not often used in the social, behavioral or biomedical sciences in spite of several potential applications in these fields. The proposed methodology addresses the complications associated with multi-factor experiments in the aforesaid fields and has a wide range of applications. It can be applied, for example, to assess the impact of several new initiatives on high-school education; or to conduct cost-effective clinical trials to study individual and combined effects of different treatments offered to patients suffering from a certain disease; or to identify critical factors that affect yield of complex physical processes in material science like synthesis of nanostructures. It can also be applied to comparative effectiveness research (e.g., in evidence-based medicine).