Spirtes相关文献与解析，生知库 | 链接生命科学的每一步

Corrigendum to "Estimating bounds on causal effects in high-dimensional and possibly confounded systems" [Int. J. Approx. Reason. 88 (2017) 371-384]

对“估计高维和可能混淆系统中因果效应的界限”的更正[国际近似推理杂志 88 (2017) 371-384]

期刊:

影响因子:

doi:10.1016/j.ijar.2025.109475

Malinsky, Daniel; Spirtes, Peter

发表日期：2025

文献求助收藏

Causal discovery and epidemiology: a potential for synergy

因果发现与流行病学：潜在的协同作用

期刊:American Journal of Epidemiology

影响因子:4.8

doi:10.1093/aje/kwae101

Petersen, Anne Helby; Ekstrøm, Claus Thorn; Spirtes, Peter; Osler, Merete

发表日期：2024

文献求助收藏

Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches

构建因果生命历程模型：数据驱动方法与理论驱动方法的比较研究

期刊:American Journal of Epidemiology

影响因子:4.8

doi:10.1093/aje/kwad144

Petersen, Anne Helby; Ekstrøm, Claus Thorn; Spirtes, Peter; Osler, Merete

发表日期：2023

文献求助收藏

Review of Causal Discovery Methods Based on Graphical Models

基于图形模型的因果发现方法综述

期刊:Frontiers in Genetics

影响因子:2.8

doi:10.3389/fgene.2019.00524

Glymour, Clark; Zhang, Kun; Spirtes, Peter

发表日期：2019

文献求助收藏

Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion

利用基于测试的删除方法对非随机缺失值进行快速因果推断

期刊:International Journal of Data Science and Analytics

影响因子:2.8

doi:10.1007/s41060-017-0094-6

Strobl, Eric V; Visweswaran, Shyam; Spirtes, Peter L

发表日期：2018

文献求助收藏

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

估计高维且可能存在混淆的系统中因果效应的界限

期刊:

影响因子:

doi:10.1016/j.ijar.2017.06.005

Malinsky, Daniel; Spirtes, Peter

发表日期：2017

文献求助收藏

Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints

通过贝叶斯独立性约束评分发现包含潜在变量的因果模型

期刊:

影响因子:

doi:10.1007/978-3-319-71246-8_9

Jabbari, Fattaneh; Ramsey, Joseph; Spirtes, Peter; Cooper, Gregory

发表日期：2017

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VIOLIN: A modular framework for scalable reconciliation of heterogeneous interaction graphs

VIOLIN：用于可扩展地协调异构交互图的模块化框架

Corrigendum to "Estimating bounds on causal effects in high-dimensional and possibly confounded systems" [Int. J. Approx. Reason. 88 (2017) 371-384]

对“估计高维和可能混淆系统中因果效应的界限”的更正[国际近似推理杂志 88 (2017) 371-384]

Causal discovery and epidemiology: a potential for synergy

因果发现与流行病学：潜在的协同作用

Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches

构建因果生命历程模型：数据驱动方法与理论驱动方法的比较研究

Review of Causal Discovery Methods Based on Graphical Models

基于图形模型的因果发现方法综述

Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion

利用基于测试的删除方法对非随机缺失值进行快速因果推断

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

估计高维且可能存在混淆的系统中因果效应的界限

Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints

通过贝叶斯独立性约束评分发现包含潜在变量的因果模型