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QuBBD: Matching Methods for causaul inference: big data and network

QuBBD：因果推理的匹配方法：大数据和网络

基本信息

批准号：
9767185
负责人：
Alexander Volfovsky
金额：
$ 27.33万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-21 至 2021-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9767185
关键词：
Address Affect Algorithms Behavior Behavioral Big Data Biological Biological Process Biometry Categories Characteristics Clinical Communicable Diseases Complex Controlled Clinical Trials Data Data Analyses Data Set Databases Development Dimensions Disease Disease Outbreaks Effectiveness of Interventions Evaluation Friendships Goals Individual Influenza Information Networks Intervention Lead Left Literature Location Measures Methodology Methods Mining Modeling Modernization Observational Study Policies Population Property Public Health Randomized Recommendation Reporting Research Running Social Network Structure Students Surveys Symptoms Techniques Time authority base college computerized tools contagion disease transmission efficacy evaluation experimental study flu insight pandemic disease preservation prevent psychologic public health intervention statistics tool

项目摘要

It is well known that causal analysis frequently suffers when relevant variables are left unobserved. Because of this, many modern public health datasets have started including massive quantities of previously unavailable information on each individual. For example, a recent study of flu-like-illness spread on college campuses has collected numerous different static and dynamic networks, biometric information, as well as standard demographic data for each individual. This project develops new statistical and computational tools that incorporate these new data structures into the evaluation of different interventions and produce interpretable causal analyses. Typical approaches to such causal analyses rely on strong modeling assumptions and dimension-reduction techniques that throw away relevant information about individuals and can lead to biased causal estimates. For example, when network information is collected it is frequently reduced to egocentric summaries that do not reflect the overall network structure. The goals of this project are as follows: 1. Develop fast almost-matching-exactly algorithms that construct matched sets for causal inference in massive datasets. 2. Develop methods for matching on available network information in order to better understand how biological processes spread. These tools are widely applicable and may lead to new insights into complex causal mechanisms. In particular this study will evaluate the efficacy of isolation interventions on flu-like-illness spread and propose new and efficient interventions to battle pandemic spread.

众所周知，当相关变量没有被观察到时，因果分析经常会受到影响。正因为如此，许多现代公共卫生数据集已经开始包括大量以前无法获得的关于每个人的信息。例如，最近一项关于流感样疾病在大学校园传播的研究收集了许多不同的静态和动态网络、生物统计信息以及每个人的标准人口统计数据。该项目开发了新的统计和计算工具，将这些新的数据结构纳入对不同干预措施的评价，并产生可解释的因果分析。这种因果分析的典型方法依赖于强大的建模假设和降维技术，这些假设和降维技术丢弃了有关个人的相关信息，并可能导致有偏见的因果估计。例如，当收集网络信息时，它经常被简化为不反映整体网络结构的以自我为中心的摘要。该项目的目标如下： 1.提出了一种用于海量数据集因果推理的几乎完全匹配的快速算法。 2.开发匹配可用的网络信息的方法，以便更好地了解生物过程是如何传播的。这些工具被广泛应用，并可能导致对复杂的因果机制的新见解。特别是，这项研究将评估隔离干预措施对流感样疾病传播的有效性，并提出新的有效干预措施来抗击大流行传播。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances

超越出处：用基于模式的平衡解释查询答案

DOI：
10.1145/3299869.3300066
发表时间：
2019
期刊：
SIGMOD
影响因子：
0
作者：
Miao, Zhengjie;Zeng, Qitian;Glavic, Boris;Roy, Sudeepa
通讯作者：
Roy, Sudeepa

Postoperative Outcomes in Infants Undergoing ABO-incompatible Heart Transplantation in the United States.

DOI：
10.1016/j.athoracsur.2021.08.039
发表时间：
2022-11
期刊：
The Annals of thoracic surgery
影响因子：
0
作者：
Chauhan D;Orlandi V;Rajab TK;Bedeir K;Volfovsky A;Mokashi S
通讯作者：
Mokashi S

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.

DOI：
10.1038/s42256-019-0048-x
发表时间：
2019-05
期刊：
NATURE MACHINE INTELLIGENCE
影响因子：
23.8
作者：
Rudin, Cynthia
通讯作者：
Rudin, Cynthia

RATest: Explaining Wrong Relational Queries Using Small Examples

RATest：使用小例子解释错误的关系查询

DOI：
10.1145/3299869.3320236
发表时间：
2019
期刊：
SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data
影响因子：
0
作者：
Miao, Zhengjie;Roy, Sudeepa;Yang, Jun
通讯作者：
Yang, Jun

Explaining Wrong Queries Using Small Examples

使用小例子解释错误的查询

DOI：
10.1145/3299869.3319866
发表时间：
2019
期刊：
SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data
影响因子：
0
作者：
Miao, Zhengjie;Roy, Sudeepa;Yang, Jun
通讯作者：
Yang, Jun