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International Journal of Data Science and Analytics
Erschienen in: International Journal of Data Science and Analytics 2/2017

28.12.2016 | Regular Paper

Introduction to the foundations of causal discovery

verfasst von: Frederick Eberhardt

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 2/2017

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Abstract

This article presents an overview of several known approaches to causal discovery. It is organized by relating the different fundamental assumptions that the methods depend on. The goal is to indicate that for a large variety of different settings the assumptions necessary and sufficient for causal discovery are now well understood.
Vorheriger Artikel Guest editorial: special issue on causal discovery
Nächster Artikel Weakening faithfulness: some heuristic causal discovery algorithms

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Fußnoten
1
See a discussion of this example in Scientific American [22].
 
2
In a somewhat counter-intuitive usage of terms, a vertex is also its own ancestor and its own descendent, but not its own parent or child.
 
3
In order to separate out limitations and sources of error in the overall inference it can be helpful to make the following three-way distinction: Statistical inference concerns the inference from data to the generating distribution or properties of the generating distribution, such as parameter values or (in)dependence relations. Causal discovery concerns the inference of identifying as much as possible about the causal structure given the statistical quantities, such as a probability distribution or its features. Causal inference concerns the determination of quantitative causal effects given the causal structure and associated statistical quantities. Of course, these three inference steps are not always completely separable and there are plenty of interesting approaches that combine them.
 
4
This example is taken from [12].
 
5
Especially with regard to the assumption of acyclicity it is worth noting that very subtle issues arise both about what exactly we mean when we allow for causal cycles, and how one may infer something about a system in which there are such feedback loops. The interested reader is encouraged to purse the references on cyclic models mentioned below.
 
6
An explicit statement of the condition is omitted here as it requires a fair bit of notation and no further insight is gained by just stating it. The intrigued reader should refer to the original paper, which is a worthwhile read in any case.
 
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Metadaten
Titel
Introduction to the foundations of causal discovery
verfasst von
Frederick Eberhardt
Publikationsdatum
28.12.2016
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 2/2017
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-016-0038-6

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