A Survey on Causal Discovery: Theory and Practice

Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that co...

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Vydáno v:International journal of approximate reasoning Ročník 151; s. 101 - 129
Hlavní autoři: Zanga, Alessio, Ozkirimli, Elif, Stella, Fabio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.12.2022
Témata:
Causal discovery
Causal models
Causality
Structural learning
Causality
Structural learning
Causal models
Causal discovery
ISSN:0888-613X, 1873-4731
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Shrnutí:Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2022.09.004