Development of Methodologies to Formalize the Informal Rules of Causal Inference from Observational Studies Using Evidence Factors and Modern Optimization

观察性研究相对便宜，但往往有缺陷，可以替代随机实验来检验治疗的因果效应。观察性研究可能是有缺陷的，因为在治疗之前，观察到的治疗组可能与未治疗组不具有可比性，这可能导致对治疗效果的偏见估计。例如，观察性研究表明，激素替代疗法可以预防绝经后女性的心脏病发作，而随机试验则显示情况并非如此。尽管如此，在许多情况下，观察性研究都提供了强有力的统计证据来支持干预措施的实施，例如当观察性研究提供了吸烟导致肺癌的强有力证据时。近年来，观察性研究提供的证据表明，青少年吸电子烟对肺部疾病有严重影响，这导致了遏制青少年吸电子烟的政策干预。但观察性研究证据的强度在很大程度上取决于非正式/半正式规则。例如，当在许多独立进行的研究中看到类似的治疗效果时，证据被认为是更有力的。在评估统计证据期间，如何使用规则通常不透明，因此，人们应该对因果关系主张的证据有多可靠持谨慎态度往往是不透明的。该项目旨在通过发展统计方法，使加强来自观测研究的科学证据的一些现有非正式规则正规化，从而使来自观测研究的证据有多强变得更加透明。为了增加这些方法的可及性，PI还将在研究生的帮助下，通过软件、课程和项目将这些方法纳入到向来自不同经验领域的研究生教授的课程中。该项目将开发几种方法来扩大证据因素在观察性研究设计中的使用范围。证据因素分析建立了统计上独立的证据(称为证据因素)，这些证据如果脆弱，就会以不同的方式容易受到潜在偏见的影响。PI将开发在新的研究设计中进行证据因素分析的方法，例如事件研究。设计和研究分析的质量将通过统计能力和设计敏感度进行评估。如果仅在现有的研究设计下考虑，证据因素的范围是有限的。这笔赠款的长期目标是开发新的和改进的观察性研究设计，其中包括证据因素分析。这些设计的构建通常需要解决NP-Hard问题。例如，证据因素可以建立在分层设计中，但创建这样的设计，同时控制许多混杂因素，需要解决NP难的图划分问题。PI将开发近似算法，使用离散和组合优化方法来解决这些设计问题。这些算法可能也会吸引应用数学界。该项目还将为复合研究中的稳健推理开发证据因素分析，这些综合研究在一个设计和分析中结合了不同研究的各个方面。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Observational studies are relatively inexpensive, but often flawed, substitutes for randomized experiments to examine the causal effect of a treatment. An observational study may be flawed because, before treatment, the observed treated group may not have been comparable to the untreated group, which can lead to a biased estimation of a treatment effect. As one example, observational studies suggested hormone replacement therapy prevents heart attacks among postmenopausal women, while randomized trials showed otherwise. Still, on multiple occasions, observational studies have provided strong statistical evidence to support implementation of an intervention, such as when observational studies provided strong evidence that smoking causes lung cancer. In recent years, observational studies have provided evidence that teenage vaping has a serious effect on lung disease which has led to policy interventions to curb teenage vaping. But the strength of observational study evidence is judged largely by informal/semi-formal rules. For example, the evidence is considered stronger when a similar treatment effect is seen across many independently conducted studies. How the rules that are used is not typically transparent during the assessment of the statistical evidence, and thus, how cautious one should be about how solid the evidence is for a causal claim is often not transparent. This project aims to make how strong the evidence is from observational studies more transparent by developing statistical methodologies to formalize some of the existing informal rules on strengthening scientific evidence from observational studies. To increase their accessibility, the PI, with help from a graduate student, will also incorporate, through software, lessons and projects, these methods in courses taught to graduate students from different empirical fields. This project will develop several methods for expanding the scope of use of evidence factors in observational study designs. An evidence factors analysis builds statistically independent pieces of evidence (called evidence factors) which, if vulnerable, are vulnerable differently to potential biases. The PI will develop methodologies for evidence factors analysis in novel study designs, such as event studies. The quality of a design and an analysis of a study will be evaluated by statistical power and design sensitivity. The scope of evidence factors is limited if considered only under existing study designs. This grant has the long-term goal of developing new and improved observational study designs which incorporate evidence factors analysis. Construction of these designs typically requires solving NP-hard problems. For example, evidence factors can be built in stratified designs, but creating such a design, while controlling for many confounders, requires solving an NP-hard graph partitioning problem. The PI will develop approximation algorithms to solve these design problems using discrete and combinatorial optimization methods. These algorithms will likely also appeal to the applied mathematics community. This project will also develop evidence factors analysis for robust inference in composite studies which combine, in one design and analysis, aspects of different studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.