| Description |
1 online resource (40 pages) |
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text file |
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74.99 |
| Summary |
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. |
| Subject |
Estimation theory.
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Conditional expectations (Mathematics)
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Effect sizes (Statistics)
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Acyclic models.
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Causation -- Mathematical models.
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Inference -- Mathematical models.
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R (Computer program language)
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Théorie de l'estimation. |
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Espérances conditionnelles (Mathématiques) |
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Ampleur de l'effet (Statistique) |
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Modèles acycliques. |
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Inférence (Logique) -- Modèles mathématiques. |
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R (Langage de programmation) |
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Acyclic models |
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Causation -- Mathematical models |
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Conditional expectations (Mathematics) |
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Effect sizes (Statistics) |
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Estimation theory |
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Inference -- Mathematical models |
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R (Computer program language) |
| Added Author |
Loukides, Mike, author.
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O'Reilly for Higher Education (Firm), distributor.
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Safari, an O'Reilly Media Company.
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| ISBN |
9781098118990 |
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1098118995 |
| Standard No. |
9781098118990 |
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