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

使用证据因素和现代优化开发观察研究中非正式因果推理规则形式化的方法

基本信息

批准号：
2015250
负责人：
Bikram Karmakar
金额：
$ 13.72万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015250&HistoricalAwards=false
关键词：
Development Methodologies Formalize Informal Rules

项目摘要

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.

观察性研究相对便宜，但往往存在缺陷，可以替代随机实验来检查治疗的因果效应。观察性研究可能存在缺陷，因为在治疗前，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倚估计。例如，观察性研究表明，激素替代疗法可以预防绝经后妇女的心脏病发作，而随机试验则表明并非如此。尽管如此，在许多情况下，观察性研究提供了强有力的统计证据来支持实施干预措施，例如观察性研究提供了吸烟导致肺癌的有力证据。近年来，观察性研究提供的证据表明，青少年vaping对肺部疾病有严重影响，这导致了政策干预，以遏制青少年vaping。但观察性研究证据的强度主要是通过非正式/半正式规则来判断的。例如，当在许多独立进行的研究中观察到类似的治疗效果时，证据被认为是更有力的。在评估统计证据的过程中，如何使用规则通常是不透明的，因此，对于因果关系索赔的证据有多可靠，人们应该有多谨慎，往往是不透明的。该项目旨在通过制定统计方法，使一些现有的关于加强观察性研究的科学证据的非正式规则正规化，从而使观察性研究的证据的有力程度更加透明。为了提高这些方法的可及性，PI在一名研究生的帮助下，还将通过软件、课程和项目，将这些方法纳入教授给来自不同经验领域的研究生的课程中。该项目将开发几种方法，以扩大观察性研究设计中证据因素的使用范围。证据因素分析建立了统计上独立的证据（称为证据因素），如果脆弱，则易受潜在偏见的影响。PI将开发新研究设计（如事件研究）中证据因素分析的方法。将通过统计功效和设计敏感性评价研究的设计和分析质量。如果仅在现有研究设计下考虑，则证据因素的范围有限。该补助金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。这些设计的构造通常需要解决NP难问题。例如，证据因子可以构建在分层设计中，但是创建这样的设计，同时控制许多混杂因素，需要解决NP-难图划分问题。PI将开发近似算法，使用离散和组合优化方法解决这些设计问题。这些算法可能也会吸引应用数学界。该项目还将开发证据因素分析，用于复合研究中的稳健推理，该复合研究将联合收割机结合在一个设计和分析中，不同研究的各个方面。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。