时间序列因果推断

本文是关于时间序列因果推断的文献综述。

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博客综述 by Shay Palachy

综述博客 Inferring causality in time series data A concise review of the major approaches. 写得非常不错！

有两种类型的因果推断，本文研究时间序列数据因果推断：

  • Causal inference over random variables, representing different events.

  • Causal inference over time series data (and thus over stochastic processes).

这篇文章的目的： This post is meant to provide a concise technical review of the major approaches found in academic literature and online resources for the purpose of inferring causality in time series data, the methods derived from them and their implementation in code form. （包括两个方面的方法：传统统计方法和其他领域方法）It aims to touch upon both (1) classical statistical approaches, created mainly in the econometrics field of research, including modern developments (2) and adaptions and original approaches coming from various other research communities, such as those dealing with dynamic systems or information theory.

Table of Contents

1. Background: Notions of causality in time series data
   包括四种不同的因果： Granger, Sims, Structural, Intervention Causality
2. Classical methods for causality inference in time series data
3. Alternative parametric Granger causality measures for time series data
4. Alternative non-parametric causality measures for time series data
5. Chaos and dynamic system theory approaches for causality inference in time series data
6. Information theoretic approaches to causality inference in time series data
7. Graphical approaches for causality inference in time series data
8. Choosing which approach to use
9. Researchers to follow
10. Other notable literature
11. References

各种因果关系：

  • Granger causality 的严格数学定义

  • Sims 因果

  • 结构因果

  • 干预因果

注意：干预因果关系的概念与此处介绍的其他三个概念有根本的不同。格兰杰因果关系，西姆斯因果关系和结构因果关系均假设为观察框架，而干预因果关系则更强有力地假设可以在研究过程中进行干预。因此，它在许多实际场景中的适用性大大降低。

经济学中的因果推断

Causal Inference and Data-Fusionin Econometrics 是在披着经济学的皮讲解着 Causal AI 如何解决 confounding bias, selection bias and 迁移学习这个难题。

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