Elements of Causal Inference : Foundations and Learning Algorithms

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-containe...

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Bibliographic Details
Main Author:	Peters, Jonas (auth)
Other Authors:	Janzing, Dominik (auth), Schölkopf, Bernhard (auth)
Format:	eBook
Published:	Cambridge The MIT Press 2017
Subjects:	Mobile & handheld device programming / Apps programming Machine learning Neural networks & fuzzy systems Causality machine learning statistical models probability theory statistics assumptions cause-effect models interventions counterfactuals SCMs identifiability semi-supervised learning covariate shift multivariate causal models markov faithfulness causal minimality do-calculus falsifiability potential outcomes algorithmic independence half-sibling regression episodic reinforcement learning domain adaptation simpson's paradox conditional independence computer science
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Elements of Causal Inference : Foundations and Learning Algorithms

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