用贝叶斯因子量化支持和反对格兰杰因果关系的证据
Zita Oravecz1, Joachim Vandekerckhove2
1Human Development and Family Studies, Pennsylvania State University.
Multivariate behavioral research
|June 9, 2023
概括
这项研究为格兰杰因果关系引入了一个新的贝叶斯因子,为时间预测关系提供了支持或反对的持续证据. 这种方法对于多层次建模和分析时间序列数据中的复杂因果关联特别有用.
科学领域:
- 时间序列分析时间序列分析
- 因果推理因果推理
- 统计建模 统计建模
背景情况:
- 格兰杰因果关系测试传统上使用零假设测试,将结论限制在拒绝或未能拒绝零假设.
- 在古典框架内,接受没有格兰杰因果关系的零假设是不可能的,这阻碍了诸如证据集成和特征选择等应用.
- 现有的方法不足以表达反对某个协会的证据或处理复杂的多层次数据结构.
研究的目的:
- 在多层次建模框架内,为格兰杰因果关系推导和实现贝叶斯因子.
- 为Granger因果关系的存在或不存在提供连续缩放的证据比率.
- 将格兰杰因果关系测试扩展到多层次的概括,以改善用稀缺或杂数据和人口水平趋势的推断.
主要方法:
- 对于格兰杰因果关系的贝叶斯因子的推导.
- 在多层次建模框架内实现.
- 适用于日常生活研究,探索影响的因果关系.
主要成果:
- 开发的贝叶斯因子为格兰杰因果关系提供了持续的证据.
- 多层次的概括可以在复杂的数据集中进行可靠的推断.
- 该方法允许表达反对格兰杰因果关系的证据,克服传统方法的局限性.
结论:
- 格兰杰因果关系的贝叶斯因数为评估时间预测关系提供了更灵活和更有信息的方法.
- 多级格兰杰因果关系测试在复杂的,层次化的数据中增强了因果推理.
- 这种方法对于需要细微证据评估的应用非常有价值,例如特征选择和证据集成.
相关概念视频
01:28Criteria for Causality: Bradford Hill Criteria - II
387
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
387
01:30Criteria for Causality: Bradford Hill Criteria - I
360
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
360
01:21Causality in Epidemiology
509
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
509
01:02Quantifying and Rejecting Outliers: The Grubbs Test
1.7K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.7K
01:27Correlation and Causation
37.8K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
37.8K
01:21Friedman Two-way Analysis of Variance by Ranks
256
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
256