Causal Inference from Two-level Factorial Designs

研究人员开发了一个从两水平析因设计和部分析因设计进行因果推断的框架，对社会、行为和生物医学科学的应用特别敏感。该框架利用了处于因果推断中心阶段的潜在结果的概念，并将Neyman的重复抽样方法和基于Fisher尖锐零假设的随机化检验扩展到两水平析因实验的情况。该框架允许从有限总体进行统计推断，允许定义和估计除“平均阶乘效应”以外的参数，并导致比基于线性模型的普通最小二乘估计的推断程序更灵活。由于随机化的限制，当调查变成观察性研究而不是随机化的析因实验时，它也确保了统计推断的有效性。析因设计允许有效和经济有效地评估几个因素及其对感兴趣的输出变量的交互作用的相对影响。这种设计已经成功地应用于几个科学、工程和工业领域，但并不经常用于社会、行为或生物医学科学，尽管在这些领域有几个潜在的应用。所提出的方法解决了上述领域中多因素实验的复杂性，具有广泛的应用前景。例如，它可以用于评估几项新举措对高中教育的影响；或者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方法的单独和联合影响；或者识别影响材料科学中复杂物理过程的关键因素，如纳米结构的合成。它还可以应用于比较有效性研究(例如，在循证医学中)。

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).