What Is Causal Inference? /

Causal inference lies at the heart of our ability to understand why things happen by helping us predict the result of any action. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a princip...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Bowne-Anderson, Hugo (Autor), Loukides, Mike (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: O'Reilly Media, Inc., 2022.
Edición:1st edition.
Temas:
Estimation theory.
Conditional expectations (Mathematics)
Effect sizes (Statistics)
Acyclic models.
Causation > Mathematical models.
Inference > Mathematical models.
R (Computer program language)
Théorie de l'estimation.
Espérances conditionnelles (Mathématiques)
Ampleur de l'effet (Statistique)
Modèles acycliques.
Inférence (Logique) > Modèles mathématiques.
R (Langage de programmation)
Acyclic models
Causation > Mathematical models
Estimation theory
Inference > Mathematical models
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario:Causal inference lies at the heart of our ability to understand why things happen by helping us predict the result of any action. This process is vital for businesses that aspire to turn data and information into valuable knowledge. With this report, data scientists and analysts will learn a principled way of thinking about causality using a suite of causal inference techniques now available. Authors Hugo Bowne-Anderson, a data science consultant, and Mike Loukides, vice president of content strategy at O'Reilly Media, introduce causality and discuss randomized control trials (RCTs), key aspects of causal graph theory, and well-needed techniques from econometrics.
Descripción Física:1 online resource (40 pages)
ISBN:9781098118990
1098118995

Ejemplares similares