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

基本信息

  • 批准号:
    2338760
  • 负责人:
  • 金额:
    $ 45万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-07-01 至 2029-06-30
  • 项目状态:
    未结题

项目摘要

Causal inference refers to a systematic way of deciphering causal relationships between entities from empirical observations – an epistemic framework that underlies past, present, and future scientific and social development. For designing statistical methods for causal inference, the gold standard pertains to randomized clinical trials where the researcher assigns treatment/exposure to subjects under study based on pure chance mechanisms. The random assignment negates systematic bias between the observed relationship between the treatment/exposure and outcome due to unknown common factors referred to as confounders. However, randomized clinical trials are often infeasible, expensive, and ethically challenging. In contrast, modern technological advancement has paved the way for the collection of massive amounts of data across a spectrum of possibilities such as health outcomes, environmental pollution, medical claims, educational policy interventions, and genetic mutations among many others. Since accounting for confounders in such data is the fundamental aspect of conducting valid causal inference, one of the major foci of modern causal inference research have been to design procedures to account for complex confounding structures without pre-specifying unrealistic statistical models. Despite the existence of a large canvas of methods in this discourse, the complete picture of the best statistical methods for inferring the causal effect of an exposure on an outcome while adjusting for arbitrary confounders remains largely open. Moreover, there are several popularly used methods that require rigorous theoretical justification and subsequent modification for reproducible statistical research in the domain of causal inference. This project is motivated by addressing these gaps and will be divided into two broad interconnected themes. In the first part, this project provides the first rigorous theoretical lens to the most popular method of confounder adjustment in large-scale genetic studies to find causal variants of diseases. This will in turn bring forth deeper questions about optimal statistical causal inference procedures that will be explored in the second part of the project. Since the project is designed to connect ideas from across statistical methods, probability theory, computer science, and machine learning, it will provide unique learning opportunities to design new courses and discourses. The project will therefore integrate research with education through course development, research mentoring for undergraduate and graduate students, especially those from underrepresented groups, and summer programs.This project will focus on two broad and interrelated themes tied together by the motivation of conducting statistical and causal inference with modern observational data. The first part of the project involves providing the first detailed theoretical picture of the most popular principal component-based method of population stratification adjustment in genome-wide association studies. This part of the project also aims to provide new methodologies to correct for existing and previously unknown possible biases in the existing methodology as well as guidelines for practitioners for choosing between methods and design of studies. By recognizing the fundamental tenet of large-scale genetic data analysis as the identification of causal genetic determinants of disease phenotypes, the second part of the project develops the first complete picture of optimal statistical inference of causal effects in both high-dimensional under sparsity and nonparametric models under smoothness conditions. Moreover, this part of the project responds to the fundamental question of tuning learning algorithms for estimating nuisance functions, such as outcome regression and propensity score for causal effect estimation, to optimize the downstream mean-squared error of causal effect estimates instead of prediction errors associated with these regression functions. The overall research will connect ideas from high-dimensional statistical inference, random matrix theory, higher-order semiparametric methods, and information theory.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.
因果推理是指从经验观察中破译实体之间因果关系的系统方法,这是过去、现在和未来科学和社会发展的基础的认知框架。为了设计因果推理的统计方法,黄金标准涉及随机临床试验,其中:研究人员根据纯粹的机会机制将治疗/暴露分配给研究对象,随机分配消除了由于称为混杂因素的未知共同因素而观察到的治疗/暴露与结果之间的系统偏差。随机临床试验往往不可行、昂贵且在道德上具有挑战性,相反,现代技术进步为收集各种可能性的大量数据铺平了道路,例如健康结果、环境污染、医疗索赔、教育政策干预。由于解释此类数据中的混杂因素是进行有效因果推理的基本方面,现代因果推理研究的主要焦点之一是设计程序来解释复杂的混杂结构,而无需预先指定。不切实际的统计尽管在这个讨论中存在大量的方法,但在调整任意混杂因素的同时推断暴露对结果的因果影响的最佳统计方法的完整情况仍然是开放的。因果推理领域的可重复统计研究需要严格的理论论证和后续修改的方法,该项目的动机是解决这些差距,并将分为两个广泛的相互关联的主题。在第一部分中,该项目提供了第一个严格的理论。镜头到最流行的方法大规模遗传研究中的混杂因素调整以寻找疾病的因果变异,这反过来会带来有关最佳统计因果推理程序的更深层次的问题,这些问题将在该项目的第二部分中进行探讨,因为该项目旨在将来自的想法联系起来。涵盖统计方法、概率论、计算机科学和机器学习,它将为设计新课程和话语整合提供独特的学习机会,因此该项目将通过课程开发、对本科生和研究生的研究指导进行教育研究,特别是来自本科生和研究生的研究指导。代表性不足的群体和暑期项目。该项目将重点关注该项目的第一部分涉及提供最流行的基于主成分的基因组群体分层调整方法的第一个详细理论图景。该项目的这一部分还旨在通过认识到研究的基本原则,提供新的方法来纠正现有方法中现有的和以前未知的可能偏差,并为从业者提供选择方法和研究设计的指南。大规模遗传数据分析作为识别疾病表型的因果决定因素,该项目的第二部分开发了稀疏条件下高维和平滑条件下非参数模型中因果效应最佳统计推断的第一个完整图片。此外,该项目遗传学的这一部分响应了基本原理。调整学习算法来估计干扰函数(例如因果效应估计的结果回归和倾向得分)的问题,以优化因果效应估计的下游均方误差,而不是与这些回归函数相关的预测误差。总体研究将连接想法。来自高维统计推断、随机矩阵理论、高阶半参数方法和信息论。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力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

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Rajarshi Mukherjee其他文献

Middle Meningeal Artery Embolization in Adjunction to Surgical Evacuation for Treatment of Subdural Hematomas: A Nationwide Comparison of Outcomes With Isolated Surgical Evacuation
脑膜中动脉栓塞联合手术清除治疗硬膜下血肿:全国范围内单独手术清除的结果比较
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    Mirhojjat Khorasanizadeh;S. Maroufi;Rajarshi Mukherjee;Madhav Sankaranarayanan;J. Moore;C. Ogilvy
  • 通讯作者:
    C. Ogilvy
Chaiqin chengqi decoction ameliorates acute pancreatitis in mice via inhibition of neuron activation-mediated acinar cell SP/NK1R signaling pathways
柴芩承气汤通过抑制神经元激活介导的腺泡细胞SP/NK1R信号通路改善小鼠急性胰腺炎
  • DOI:
    10.1016/j.jep.2021.114029
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Chenxia Han;Dan Du;Yongjian Wen;Jiawang Li;Rui Wang;Tao Jin;Jingyu Yang;Na Shi;Kun Jiang;Lihui Deng;Xianghui Fu;Rajarshi Mukherjee;John A Windsor;Jiwon Hong;Anthony R Phillips;Robert Sutton;Wei Huang;Tingting Liu;Qing Xia
  • 通讯作者:
    Qing Xia
Acinar Cell NLRP3 Inflammasome and GSDMD Activation Mediates Pyroptosis and Systemic Inflammation in Acute Pancreatitis
腺泡细胞 NLRP3 炎症小体和 GSDMD 激活介导急性胰腺炎焦亡和全身炎症
  • DOI:
    10.3167/arms.2020.030118
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    7.3
  • 作者:
    Lin Gao;Xiaowu Dong;Weijuan Gong;Wei Huang;Jing Xue;Qingtian Zhu;Nan Ma;Weiwei Chen;Xianghui Fu;Xiang Gao;Zhaoyu Lin;Yanbing Ding;Juanjuan Shi;Zhihui Tong;Tingting Liu;Rajarshi Mukherjee;Robert Sutton;Guotao Lu;Weiqin Li
  • 通讯作者:
    Weiqin Li
HIGHER ORDER ESTIMATING EQUATIONS FOR HIGH-DIMENSIONAL MODELS.
高维模型的高阶估计方程。
  • DOI:
    10.1214/16-aos1515
  • 发表时间:
    2017-10-01
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    James Robins;Lingling Li;Rajarshi Mukherjee;E. Tchetgen;A. van der Vaart
  • 通讯作者:
    A. van der Vaart
Discharge protocol in acute pancreatitis: an international survey and cohort analysis
急性胰腺炎的出院方案:一项国际调查和队列分析
  • DOI:
    10.1038/s41598-023-48480-z
  • 发表时间:
    2023-12-13
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    R. Nagy;Klementina Ocskay;Z. Sipos;A. Szentesi;Á. Vincze;L. Czakó;F. Izbéki;N. Shirinskaya;Vladimir L. Poluektov;Alexandr N. Zolotov;Yin Zhu;L. Xia;W. He;Robert Sutton;P. Szatmary;Rajarshi Mukherjee;Isobel Burridge;Emma Wauchope;Elsa Francisco;David Aparicio;Bruno Pinto;A. Gomes;Vitor Nunes;Vasile Marcel Tantau;Emanuela Denisa Sagau;A. Tanțău;A. Suceveanu;C. Tocia;Andrei Dumitru;Elizabeth Pando;Piero Alberti;A. Cirera;Xavier Molero;Hong Sik Lee;M. K. Jung;Eui Joo Kim;Sanghyub Lee;María Lourdes Ruiz Rebollo;Reyes Busta Nistal;Sandra Izquierdo Santervás;Dušan Leško;M. Soltes;Jozef Radonak;H. Zatorski;E. Małecka;A. Fabisiak;M. S. Yaroslav;V. M. Mykhailo;A. T. Olekcandr;G. Barauskas;Vytautas Simanaitis;P. Ignatavičius;M. Jinga;V. Balaban;C. Patoni;Liang Gong;Kai Song;Yunlong Li;T. C. Gonçalves;Marta Freitas;Vítor Macedo;Marlies Vornhuelz;S. Klauss;Georg Beyer;A. Koksal;M. Tozlu;A. T. Eminler;N. T. Monclus;E. Comas;Juan Armando Rodriguez Oballe;Łukasz Nawacki;Stanisław Głuszek;Alberto Rama;Marco Galego;D. de la Iglesia;Umut Emre Aykut;D. Duman;Rahmi Aslan;A. Gherbon;L. Deng;Wei Huang;Qing Xia;G. Poropat;A. Radovan;L. Vranic;C. Ricci;C. Ingaldi;R. Casadei;Ionut Negoi;Cezar Ciubotaru;Florin;Gabriel Constantinescu;V. Şandru;E. Altıntaş;Hatice Rizaoglu Balci;J. Constantino;D. Aveiro;Jorge Pereira;Süleyman Gunay;Seda Misirlioglu Sucan;Oleksiy Dronov;I. Kovalska;Nikhil Bush;S. Rana;Serge Chooklin;S. Chuklin;I. Saizu;Cristian Gheorghe;Philipp Göltl;Michael Hirth;R. Mateescu;Geanina Papuc;G. Minkov;Emil Enchev;L. Mastrangelo;E. Jovine;Weiwei Chen;Quping Zhu;A. Gąsiorowska;Natalia Fabisiak;M. Bezmarević;Andrey Litvin;M. C. Mottes;E. Choi;Peter Bánovčin;Lenka Nosáková;M. Kovacheva;A. Kchaou;Ahmed Tlili;M. Marino;Katarzyna Kusnierz;A. Mickevicius;M. Hollenbach;P. Molcan;Orestis Ioannidis;M. Tokarev;A. Ince;I. Semenenko;S. Galeev;E. Ramírez;V. Sallinen;Petr Pěnčík;J. Bajor;P. Sarlós;R. Hágendorn;S. Gódi;I. Szabó;J. Czimmer;G. Pár;A. Illés;N. Faluhelyi;P. Kanizsai;Tamás Nagy;A. Mikó;B. Németh;J. Hamvas;B. Bod;M. Varga;I. Török;János Novák;Á. Patai;János Sümegi;Csaba Góg;Mária Papp;B. Erőss;S. Váncsa;B. Teutsch;K. Márta;P. Hegyi;T. Tornai;Balázs Lázár;T. Hussein;Dorottya Tarján;Mónika Lipp;Beáta Kovács;Orsolya Urbán;Emese Fürst;E. Tari;Ibolya Kocsis;Pál Maurovich;B. Tihanyi;Orsolya Eperjesi;Zita Kormos;Pál Ákos Deák;A. Párniczky;P. Hegyi
  • 通讯作者:
    P. Hegyi

Rajarshi Mukherjee的其他文献

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{{ truncateString('Rajarshi Mukherjee', 18)}}的其他基金

Causal Inference and Machine Learning Methods
因果推理和机器学习方法
  • 批准号:
    1941419
  • 财政年份:
    2020
  • 资助金额:
    $ 45万
  • 项目类别:
    Standard Grant

相似国自然基金

极大孔径多天线系统基于结构化统计推理的非平稳信道估计和干扰抑制技术
  • 批准号:
  • 批准年份:
    2021
  • 资助金额:
    30 万元
  • 项目类别:
    青年科学基金项目
面向大规模演化异质信息网络的未知关系学习与推理研究
  • 批准号:
    61876183
  • 批准年份:
    2018
  • 资助金额:
    62.0 万元
  • 项目类别:
    面上项目
基于统计的类型推理方法研究
  • 批准号:
    61872272
  • 批准年份:
    2018
  • 资助金额:
    63.0 万元
  • 项目类别:
    面上项目
面向智能推理的逻辑增强型分布式知识表示研究
  • 批准号:
    61876223
  • 批准年份:
    2018
  • 资助金额:
    65.0 万元
  • 项目类别:
    面上项目
基于逻辑规则和表示学习的知识图谱关系推理方法与应用研究
  • 批准号:
    61772117
  • 批准年份:
    2017
  • 资助金额:
    66.0 万元
  • 项目类别:
    面上项目

相似海外基金

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:
    $ 45万
  • 项目类别:
    Continuing Grant
CAREER: Distribution-Free and Adaptive Statistical Inference
职业:无分布和自适应统计推断
  • 批准号:
    2338464
  • 财政年份:
    2024
  • 资助金额:
    $ 45万
  • 项目类别:
    Continuing Grant
CAREER: Statistical Inference in High Dimensions using Variational Approximations
职业:使用变分近似进行高维统计推断
  • 批准号:
    2239234
  • 财政年份:
    2023
  • 资助金额:
    $ 45万
  • 项目类别:
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CAREER: Computer-Intensive Statistical Inference on High-Dimensional and Massive Data: From Theoretical Foundations to Practical Computations
职业:高维海量数据的计算机密集统计推断:从理论基础到实际计算
  • 批准号:
    2347760
  • 财政年份:
    2023
  • 资助金额:
    $ 45万
  • 项目类别:
    Continuing Grant
CAREER: Towards Tight Guarantees of Markov Chain Sampling Algorithms in High Dimensional Statistical Inference
职业:高维统计推断中马尔可夫链采样算法的严格保证
  • 批准号:
    2237322
  • 财政年份:
    2023
  • 资助金额:
    $ 45万
  • 项目类别:
    Continuing Grant
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