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中文摘要
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众所周知,当相关变量未被观察到时,因果分析往往会受到影响。因为
英文摘要
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.
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Machine Learning and Deep Learning Solutions Supplement: Matching Methods for Causal Inference with Complex Data
  • 批准号:
    9750434
  • 项目类别:
  • 资助金额:
    $9.87万
  • 财政年份:
    2017
  • 负责人:
    Alexander Volfovsky
  • 依托单位:
QuBBD: Matching Methods for causaul inference: big data and network
  • 批准号:
    9767185
  • 项目类别:
  • 资助金额:
    $27.33万
  • 财政年份:
    2017
  • 负责人:
    Alexander Volfovsky
  • 依托单位:
ConProject-001
  • 批准号:
    9564450
  • 项目类别:
  • 资助金额:
    $11.34万
  • 财政年份:
    --
  • 负责人:
    Alexander Volfovsky
  • 依托单位:
ConProject-001
  • 批准号:
    9568757
  • 项目类别:
  • 资助金额:
    $27.91万
  • 财政年份:
    --
  • 负责人:
    Alexander Volfovsky
  • 依托单位:
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