Causal Inference with Irregularly Spaced Observation Times

不规则间隔观察时间的因果推断

基本信息

  • 批准号:
    2242776
  • 负责人:
  • 金额:
    $ 22.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-09-01 至 2026-08-31
  • 项目状态:
    未结题

项目摘要

This research project will develop a causal inference and machine learning toolset to tackle important and recurring challenges arising from emergent real-world data. Real-world data (e.g., consumer expenditures, mobile health applications, and electronic health records) provide unique opportunities for discovering optimal treatment strategies for the economy and health care. However, complex data also present novel challenges for statistical analysis. These challenges, such as irregularly spaced observation times or mixed data types, are impediments to effectively translating rich information into meaningful knowledge. This project will result in fundamental, broadly applicable advances in methodology for causal models with complex structures. It will provide principled causal inference approaches to scientific questions with complex data, such as longitudinal observational data, mobile health data, and electronic health records. The results of this research will be incorporated into graduate teaching, short courses, and workshops. Open-source software and R packages also will be developed.This research project will develop simple-to-interpret Marginal Structural Models for multinomial choices, taking into account correlations of expenditure categories, with an application to study the effect of lockdowns on consumer shopping behavior during the COVID-19 pandemic. Semiparametric doubly robust estimators will be developed to address time-varying confounding and irregularly spaced observation times, capitalizing on semiparametric efficiency theory and advanced machine learning methods. The investigator also will develop a unified framework of continuous-time Structural-Nested Models (SNMs) for general outcomes with time-varying confounding and informative observation times. The informativeness of observation times presents vital obstacles to the identification and estimation of the SNM parameter. Finally, electronic health records collect large amounts of granular patient data, which provide both opportunities and challenges for improving the assessment of treatment effects. The investigator will develop causal inference methods for estimating treatment effects with new functional principal component analysis (FPCA) of functional confounders subject to informative sampling for observations. The new FPCA also presents new prospects in the scope of functional data analysis.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.
该研究项目将开发一个因果推理和机器学习工具集,以解决现实世界数据中出现的重要和反复出现的挑战。真实世界数据(例如,消费者支出、移动的健康应用和电子健康记录)为发现经济和健康护理的最佳治疗策略提供了独特的机会。然而,复杂的数据也给统计分析带来了新的挑战。这些挑战,例如不规则间隔的观察时间或混合的数据类型,是有效地将丰富的信息转化为有意义的知识的障碍。该项目将导致具有复杂结构的因果模型方法的根本性,广泛适用的进步。它将为复杂数据的科学问题提供原则性的因果推理方法,如纵向观察数据,移动的健康数据和电子健康记录。这项研究的结果将纳入研究生教学,短期课程和研讨会。该研究项目将开发易于解释的边际结构模型(Marginal Structural Models),用于多项选择,考虑支出类别的相关性,并应用于研究COVID-19疫情期间封锁对消费者购物行为的影响。将利用半参数效率理论和先进的机器学习方法,开发半参数双重稳健估计量,以解决时变混杂和间隔不规则的观察时间。研究者还将开发一个统一的连续时间结构嵌套模型(SNM)框架,用于具有时变混杂和信息观测时间的一般结局。观测时间的信息量对SNM参数的识别和估计提出了重要的障碍。最后,电子健康记录收集了大量精细的患者数据,这为改善治疗效果评估提供了机遇和挑战。研究者将开发因果推断方法,通过对功能性混杂因素进行新的功能性主成分分析(FPCA)来估计治疗效果,并进行信息性抽样观察。新的FPCA还展示了功能数据分析领域的新前景。该奖项反映了NSF的法定使命,并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,被认为值得支持。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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

{{ 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 }}

Shu Yang其他文献

Eukaryotic community composition and dynamics during solid waste decomposition
固体废物分解过程中的真核群落组成和动态
  • DOI:
    10.1007/s00253-022-11912-3
  • 发表时间:
    2022-04
  • 期刊:
  • 影响因子:
    5
  • 作者:
    Shu Yang;Lei Li;Xuya Peng;Rui Zhang;Liyan Song
  • 通讯作者:
    Liyan Song
The effect of selenite on mercury re-emission in smelting flue gas scrubbing system
亚硒酸盐对冶炼烟气洗涤系统汞再排放的影响
  • DOI:
    10.1016/j.fuel.2015.11.072
  • 发表时间:
    2016-03
  • 期刊:
  • 影响因子:
    7.4
  • 作者:
    Bing Peng;Zhilou Liu;Liyuan Chai;Hui Liu;Shu Yang;Bentao Yang;Kaisong Xiang;Cao Liu
  • 通讯作者:
    Cao Liu
On analyzing and predicting regional taxicab service rate from trajectory data
基于轨迹数据分析预测区域出租车服务率
UIS Withstanding Capability of GaN E-HEMTs with Schottky and Ohmic p-GaN contact
具有肖特基和欧姆 p-GaN 接触的 GaN E-HEMT 的 UIS 耐受能力
The Change of GFAP or S100B Concentration in Serum Before and After Carotid Artery Stenting
颈动脉支架置入术前后血清中GFAP或S100B浓度的变化
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xiaofan Yuan;Shu Yang;Lei Guo;Duo;Jie Huang;Jianhong Wang;F. Guo
  • 通讯作者:
    F. Guo

Shu Yang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 }}

{{ truncateString('Shu Yang', 18)}}的其他基金

Design, synthesis, and assembly of composite liquid crystal elastomer fibers
复合液晶弹性体纤维的设计、合成和组装
  • 批准号:
    2104841
  • 财政年份:
    2021
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
FMRG: Threading High-Performance, Self-Morphing Building Blocks Across Scales Toward a Sustainable Future
FMRG:跨尺度构建高性能、自我变形的构建模块,迈向可持续的未来
  • 批准号:
    2037097
  • 财政年份:
    2020
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
Planning Grant: Engineering Research Center for Convergence of Scalable and Sustainable Digital Fabrication of Smart Textiles
规划资助:智能纺织品可扩展和可持续数字制造融合工程研究中心
  • 批准号:
    1937031
  • 财政年份:
    2019
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
Theory and Methods for Causal Inference in Chronic Diseases
慢性病因果推断的理论与方法
  • 批准号:
    1811245
  • 财政年份:
    2018
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
EAGER/Collaborative Research: Environmentally Responsive, Water Harvesting and Self-Cooling Building Envelopes
EAGER/合作研究:环境响应、集水和自冷却建筑围护结构
  • 批准号:
    1745912
  • 财政年份:
    2017
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
INSPIRE Track 2: Discovery and Development of Optimized Photonic Systems for High Volume, Low Surface Area Solar Energy Harvesting: Learning from Giant Clams
INSPIRE 轨道 2:发现和开发用于大容量、低表面积太阳能收集的优化光子系统:向巨蛤学习
  • 批准号:
    1343159
  • 财政年份:
    2014
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
Programmable pattern transformation of reconfigurable polymer membranes
可重构聚合物膜的可编程图案转换
  • 批准号:
    1410253
  • 财政年份:
    2014
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: Efficient Rare Cell Capturing in Microfluidic Devices via Multiscale Surface Design
合作研究:通过多尺度表面设计在微流体装置中高效捕获稀有细胞
  • 批准号:
    1263940
  • 财政年份:
    2013
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
GOALI: A Multiscale Approach on Interfacial and Structural Interlocking Between Polymer Grafted Shape Memory Pillars
GOALI:聚合物接枝形状记忆柱之间界面和结构联锁的多尺度方法
  • 批准号:
    1105208
  • 财政年份:
    2011
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
EFRI-SEED: Energy Minimization via Multi-Scaler Architectures From Cell Contractility to Sensing Materials to Adaptive Building Skins
EFRI-SEED:通过多尺度架构实现能量最小化,从细胞收缩性到传感材料再到自适应建筑表皮
  • 批准号:
    1038215
  • 财政年份:
    2010
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant

相似海外基金

CAREER: Game Theoretic Models for Robust Cyber-Physical Interactions: Inference and Design under Uncertainty
职业:稳健的网络物理交互的博弈论模型:不确定性下的推理和设计
  • 批准号:
    2336840
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Continuing Grant
Spectral embedding methods and subsequent inference tasks on dynamic multiplex graphs
动态多路复用图上的谱嵌入方法和后续推理任务
  • 批准号:
    EP/Y002113/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Research Grant
CAREER: Statistical foundations of particle tracking and trajectory inference
职业:粒子跟踪和轨迹推断的统计基础
  • 批准号:
    2339829
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Continuing Grant
Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption
合作研究:SHF:小型:基于密文-密文全同态加密的高效、可扩展的隐私保护神经网络推理
  • 批准号:
    2412357
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
Probabilistic Inference Based Utility Evaluation and Path Generation for Active Autonomous Exploration of USVs in Unknown Confined Marine Environments
基于概率推理的效用评估和路径生成,用于未知受限海洋环境中 USV 主动自主探索
  • 批准号:
    EP/Y000862/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Research Grant
CAREER: Efficient Large Language Model Inference Through Codesign: Adaptable Software Partitioning and FPGA-based Distributed Hardware
职业:通过协同设计进行高效的大型语言模型推理:适应性软件分区和基于 FPGA 的分布式硬件
  • 批准号:
    2339084
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Continuing Grant
AI4PhotMod - Artificial Intelligence for parameter inference in Photosynthesis Models
AI4PhotMod - 用于光合作用模型中参数推断的人工智能
  • 批准号:
    BB/Y51388X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Research Grant
CSR: Small: Latency-controlled Reduction of Data Center Expenses for Handling Bursty ML Inference Requests
CSR:小:通过延迟控制减少数据中心处理突发 ML 推理请求的费用
  • 批准号:
    2336886
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
CAREER: Statistical Inference in Observational Studies -- Theory, Methods, and Beyond
职业:观察研究中的统计推断——理论、方法及其他
  • 批准号:
    2338760
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Continuing Grant
STATISTICAL AND COMPUTATIONAL THRESHOLDS IN SPIN GLASSES AND GRAPH INFERENCE PROBLEMS
自旋玻璃和图推理问题的统计和计算阈值
  • 批准号:
    2347177
  • 财政年份:
    2024
  • 资助金额:
    $ 22.5万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了