Methods and tools for causal discovery and causal inference

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Nogueira, AR (Author) Other; Pugnana, A (Author) Other; Ruggieri, S (Author) Other; Pedreschi, D (Author) Other; João Gama (Author) FEP

Journal

Title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

ISSN: 1942-4787

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 82 Citations

Scopus - 115 Citations

Other information

Authenticus ID: P-00V-ZPZ

DOI: 10.1002/widm.1449

Abstract (EN): Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 39