Causal inference in network settings

网络设置中的因果推断

基本信息

  • 批准号:
    RGPIN-2019-04230
  • 负责人:
  • 金额:
    $ 3.35万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2020
  • 资助国家:
    加拿大
  • 起止时间:
    2020-01-01 至 2021-12-31
  • 项目状态:
    已结题

项目摘要

Many statistical problems aim to answer etiological questions: does exposure Z cause changes in an outcome Y, when accounting for possible biasing factors such as non-random sampling (selection bias), measurement error, missing data, or confounding? Causal inference offers a means of formalizing the assumptions that are required to draw causal conclusions from an analysis. While causal methods are routinely used for simple settings such as point or even longitudinal exposures, there are many new frontiers to explore in this exciting branch of statistics. The proposed research will tackle causality in several new settings, where traditional regression techniques are not appropriate. I will focus on three main objectives related to causal inference: (1) to extend causal methods in the context of environmental data; (2) to develop causal estimators for use in respondent-driven samples; and (3) to estimate sequential tailored decision-rules from non-experimental data that are stored across a distributed network of sites who cannot share individual-level data. These three objectives all tackle different aspect of causal inference in a network, with different challenges arising depending on the type of network structure. In environmental data, data are often spatially distributed, and the commonly-made assumption of “no interference” is violated. In respondent-driven sampling, the network structure arises due to a sampling design in which participants recruit their contacts and hence the sample units are not independent and, for some exposures, interference may again arise. The third aim tackles a very different problem: while data within a given site may be correlated, individual measures are unavailable to the analyst, and only pooled or summary information is available to learn about tailored decision strategies. The impact of this work will be to change the manner in which researchers carry out analyses designed to achieve important research goals, ranging from understanding the impact of environmental exposures on economic outcomes to better using data from multiple sites without the risk of privacy breaches that occur when full data are shared. The work will also provide important insights into the impact of correlation and complex network structures on the types of questions that can be answered causally, and the estimators used to answer them.
许多统计问题旨在回答病因学问题:当考虑可能的偏倚因素,如非随机抽样(选择偏倚)、测量误差、缺失数据或混杂因素时,暴露Z是否会导致结果Y的变化?因果推理提供了一种将从分析中得出因果结论所需的假设形式化的方法。虽然因果方法通常用于简单的设置,如点,甚至纵向曝光,有许多新的前沿探索在这个令人兴奋的统计分支。 拟议的研究将在几个新的环境中解决因果关系,传统的回归技术不适用。我将重点关注与因果推理相关的三个主要目标:(1)在环境数据的背景下扩展因果方法;(2)开发用于响应驱动样本的因果估计器;(3)从存储在分布式网络中的非实验数据中估计连续定制的决策规则,这些数据无法共享个人数据。这三个目标都解决了网络中因果推理的不同方面,根据网络结构的类型,会出现不同的挑战。在环境数据中,数据通常是空间分布的,并且通常所做的“无干扰”的假设被违反。在响应者驱动的抽样中,网络结构的出现是由于抽样设计,其中参与者招募他们的联系人,因此样本单元不是独立的,对于某些曝光,干扰可能会再次出现。第三个目标解决了一个非常不同的问题:虽然给定站点内的数据可能是相关的,但分析师无法获得单个度量,只有汇总或汇总信息可用于了解定制的决策策略。 这项工作的影响将是改变研究人员进行旨在实现重要研究目标的分析的方式,从了解环境暴露对经济成果的影响到更好地使用来自多个站点的数据,而不会在共享完整数据时发生隐私泄露的风险。这项工作还将为相关性和复杂网络结构对可以因果回答的问题类型的影响以及用于回答这些问题的估计量提供重要见解。

项目成果

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Moodie, Erica其他文献

Moodie, Erica的其他文献

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{{ truncateString('Moodie, Erica', 18)}}的其他基金

Causal inference in network settings
网络设置中的因果推断
  • 批准号:
    RGPIN-2019-04230
  • 财政年份:
    2022
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Causal inference in network settings
网络设置中的因果推断
  • 批准号:
    RGPIN-2019-04230
  • 财政年份:
    2021
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Causal inference in network settings
网络设置中的因果推断
  • 批准号:
    RGPIN-2019-04230
  • 财政年份:
    2019
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
A new framework for estimation and inference of optimal dynamic treatment regimes
最佳动态治疗方案估计和推断的新框架
  • 批准号:
    RGPIN-2014-05468
  • 财政年份:
    2018
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
A new framework for estimation and inference of optimal dynamic treatment regimes
最佳动态治疗方案估计和推断的新框架
  • 批准号:
    RGPIN-2014-05468
  • 财政年份:
    2017
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
A new framework for estimation and inference of optimal dynamic treatment regimes
最佳动态治疗方案估计和推断的新框架
  • 批准号:
    RGPIN-2014-05468
  • 财政年份:
    2016
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
A new framework for estimation and inference of optimal dynamic treatment regimes
最佳动态治疗方案估计和推断的新框架
  • 批准号:
    RGPIN-2014-05468
  • 财政年份:
    2015
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
A new framework for estimation and inference of optimal dynamic treatment regimes
最佳动态治疗方案估计和推断的新框架
  • 批准号:
    RGPIN-2014-05468
  • 财政年份:
    2014
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Optimal adaptive treatment strategies: Finding practical solutions to inferential challenges
最佳适应性治疗策略:寻找推理挑战的实用解决方案
  • 批准号:
    331271-2009
  • 财政年份:
    2013
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Optimal adaptive treatment strategies: Finding practical solutions to inferential challenges
最佳适应性治疗策略:寻找推理挑战的实用解决方案
  • 批准号:
    331271-2009
  • 财政年份:
    2012
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual

相似海外基金

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开发因果推理方法,通过社会互动评估和利用溢出效应,设计改进的艾滋病毒预防干预措施
  • 批准号:
    10762679
  • 财政年份:
    2023
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Implementing Novel Causal Evaluations in the PRACTICAL Trial and through an International Observational Data Network (INCEPTION) Project
在实践试验中并通过国际观测数据网络 (INCEPTION) 项目实施新颖的因果评估
  • 批准号:
    490293
  • 财政年份:
    2023
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Operating Grants
Causal inference in network settings
网络设置中的因果推断
  • 批准号:
    RGPIN-2019-04230
  • 财政年份:
    2022
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Causal Inference with Massive and Complex data: High-dimensionality and Network Interference
海量复杂数据的因果推理:高维和网络干扰
  • 批准号:
    RGPIN-2019-07052
  • 财政年份:
    2022
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Causal Inference with Massive and Complex data: High-dimensionality and Network Interference
海量复杂数据的因果推理:高维和网络干扰
  • 批准号:
    RGPIN-2019-07052
  • 财政年份:
    2021
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Causal inference in network settings
网络设置中的因果推断
  • 批准号:
    RGPIN-2019-04230
  • 财政年份:
    2021
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Causal Inference with Massive and Complex data: High-dimensionality and Network Interference
海量复杂数据的因果推理:高维和网络干扰
  • 批准号:
    RGPIN-2019-07052
  • 财政年份:
    2020
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
Causal Inference with Massive and Complex data: High-dimensionality and Network Interference
海量复杂数据的因果推理:高维和网络干扰
  • 批准号:
    RGPAS-2019-00093
  • 财政年份:
    2020
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Corticostriatal mechanisms of causal inference and temporal credit assignment.
因果推理和时间信用分配的皮质纹状体机制。
  • 批准号:
    10053605
  • 财政年份:
    2020
  • 资助金额:
    $ 3.35万
  • 项目类别:
Causal inference in network settings
网络设置中的因果推断
  • 批准号:
    RGPIN-2019-04230
  • 财政年份:
    2019
  • 资助金额:
    $ 3.35万
  • 项目类别:
    Discovery Grants Program - Individual
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