CAREER: Integrating Optimal Design and Inference for Modern Observational Studies

职业:将优化设计与推理相结合进行现代观察研究

基本信息

  • 批准号:
    2142146
  • 负责人:
  • 金额:
    $ 43.74万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-07-01 至 2027-06-30
  • 项目状态:
    未结题

项目摘要

This research project will develop new methods for inference about causal relationships in large administrative datasets. These datasets are an increasingly important source of evidence about causal effects in health services, public policy, and the social sciences. Procedures for measuring the causal effect of a treatment of interest emphasize creating similar subgroups of individuals, one receiving treatment and another receiving control. In practice, however, this process is not able to achieve perfect similarity in large administrative datasets, especially when the units in the study exhibit structure over time or space. The methods to be developed will produce confidence intervals and hypothesis tests that account explicitly for imperfect design and structure over units. Careful analysis of these methods will lead to valuable guidance for how to make initial designs less imperfect. The resulting tools will pair effectively with modern machine learning methods in a modular framework and will immediately be applicable to large-scale studies of health and educational outcomes. Supported educational activities will engage undergraduate researchers from diverse backgrounds, groom graduate students for future roles as faculty mentors, and produce pedagogical materials valuable for training undergraduate students in the principles of causal inference. Open-source software also will be developed.This project will focus on integrating design and inference for two widely used causal inference designs that attempt to create credible comparisons from initially different treatment and control samples: matching, which groups similar treated and control individuals together into small homogenous matched sets; and weighting, which constructs weights for study units with the goal of downplaying dissimilarities and emphasizing similarities between the groups. Four specific tasks will be undertaken. First, existing methods of permutation inference for matched designs, which reshuffle labels for treated and control units within matched groups to construct hypothesis tests, will be transformed by allowing permutation probabilities to vary according to the degree of remaining discrepancy in individual matched sets. The resulting method, which will be easy to implement using estimated probabilities of treatment, permits sensitivity analysis for unobserved variables and suggests a new method of choosing an initial match that effectively manages tradeoffs between similarity on probability of treatment and similarity on outcome risk. Second, new algorithms will be designed to efficiently sample from conditional permutation distributions that respect design constraints in matching including optimal pairing on estimated probabilities of treatment and constrained imbalance on multiple variables. These tools will lead to reduced bias and improved precision by paying attention to aspects of the study's design. Third, tools for matched permutation inference will be extended to clustered observational studies with treatment given at both cluster and individual levels and possible spillover effects. An accompanying sensitivity analysis that addresses unobserved variables at both individual and cluster levels also will be constructed. Finally, a new measure for large-sample performance of weighting methods in the presence of unobserved variables will be constructed, quantifying the impact of design choices on robustness to bias from unobserved variables.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.
该研究项目将开发新的方法来推断大型行政数据集中的因果关系。 这些数据集是关于卫生服务、公共政策和社会科学中因果效应的越来越重要的证据来源。用于测量感兴趣的治疗的因果效应的程序强调创建类似的个体亚组,一个接受治疗,另一个接受对照。 然而,在实践中,这一过程是无法实现完美的相似性,在大型管理数据集,特别是当在研究中的单位表现出结构随时间或空间。 待开发的方法将产生置信区间和假设检验,明确说明不完美的设计和结构的单位。对这些方法的仔细分析将为如何使初始设计不那么不完美提供有价值的指导。 由此产生的工具将在模块化框架中与现代机器学习方法有效配对,并将立即适用于大规模的健康和教育成果研究。 支持的教育活动将吸引来自不同背景的本科生研究人员,培养研究生未来作为教师导师的角色,并制作对培养本科生因果推理原则有价值的教学材料。该项目将着重于将两种广泛使用的因果推理设计的设计和推理结合起来,这两种设计试图从最初不同的治疗和对照样本中进行可信的比较:匹配,将相似的治疗和对照个体分组为小型同质匹配集;加权,为研究单位构建权重,目的是淡化差异,强调组间的相似性。 将开展四项具体任务。 首先,现有的方法匹配的设计,重新洗牌标签匹配组内的治疗和控制单元,以构建假设检验的置换推理,将通过允许置换概率根据个别匹配集的剩余差异的程度而变化。 由此产生的方法,这将是很容易实现的估计概率的治疗,允许未观察到的变量的敏感性分析,并建议一种新的方法,选择一个初始的匹配,有效地管理之间的权衡相似性的治疗概率和相似性的结果风险。 其次,将设计新的算法,以有效地从条件排列分布中采样,这些分布尊重匹配中的设计约束,包括估计治疗概率的最佳配对和多个变量的约束不平衡。 这些工具将通过关注研究设计的各个方面来减少偏倚并提高精度。 第三,匹配排列推理的工具将扩展到集群观察性研究,在集群和个体水平上给予治疗,并可能产生溢出效应。还将构建相应的敏感性分析,以解决个体和聚类水平上的未观察变量。 最后,一个新的衡量大样本性能的加权方法在存在未观察到的变量将被构建,量化的影响,设计选择的鲁棒性偏差从未观察到的variables.This奖项反映了NSF的法定使命,并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Robust inference for matching under rolling enrollment
滚动注册下匹配的稳健推理
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Amanda K. Glazer;Samuel D. Pimentel
  • 通讯作者:
    Samuel D. Pimentel
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Samuel Pimentel其他文献

Samuel Pimentel的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

相似海外基金

Collaborative Research: Integrating Optimal Function and Compliant Mechanisms for Ubiquitous Lower-Limb Powered Prostheses
合作研究:将优化功能和合规机制整合到无处不在的下肢动力假肢中
  • 批准号:
    2344765
  • 财政年份:
    2024
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Standard Grant
Collaborative Research: Integrating Optimal Function and Compliant Mechanisms for Ubiquitous Lower-Limb Powered Prostheses
合作研究:将优化功能和合规机制整合到无处不在的下肢动力假肢中
  • 批准号:
    2344766
  • 财政年份:
    2024
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Standard Grant
Tissue engineering of the human thymus: developing the optimal scaffold through integrating biological protocols with advanced imaging
人类胸腺组织工程:通过将生物方案与先进成像相结合来开发最佳支架
  • 批准号:
    2722998
  • 财政年份:
    2022
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Studentship
Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CT
集成人工智能以优化心脏 PET/CT 分析
  • 批准号:
    10593858
  • 财政年份:
    2022
  • 资助金额:
    $ 43.74万
  • 项目类别:
Optimal strategies for integrating waste-to-energy conversion with CO2 capture and utilization (CCU)
将废物转化为能源与二氧化碳捕获和利用 (CCU) 相结合的最佳策略
  • 批准号:
    DGECR-2022-00074
  • 财政年份:
    2022
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Discovery Launch Supplement
Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CT
集成人工智能以优化心脏 PET/CT 分析
  • 批准号:
    10708921
  • 财政年份:
    2022
  • 资助金额:
    $ 43.74万
  • 项目类别:
Optimal strategies for integrating waste-to-energy conversion with CO2 capture and utilization (CCU)
将废物转化为能源与二氧化碳捕获和利用 (CCU) 相结合的最佳策略
  • 批准号:
    RGPIN-2022-05049
  • 财政年份:
    2022
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Discovery Grants Program - Individual
EAGER: Integrating Fracture Nucleation and Propagation into Optimization: Towards Materials with Optimal Fracture Properties
EAGER:将断裂成核和扩展整合到优化中:寻找具有最佳断裂性能的材料
  • 批准号:
    2127134
  • 财政年份:
    2021
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Standard Grant
Collaborative Research: Integrating Antarctic Environmental and Biological Predictability to Obtain Optimal Forecasts
合作研究:整合南极环境和生物可预测性以获得最佳预测
  • 批准号:
    2037531
  • 财政年份:
    2021
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Standard Grant
Collaborative Research: Integrating Antarctic Environmental and Biological Predictability to Obtain Optimal Forecasts
合作研究:整合南极环境和生物可预测性以获得最佳预测
  • 批准号:
    2037561
  • 财政年份:
    2021
  • 资助金额:
    $ 43.74万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了