Elements of Causal Inference Foundations and Learning Algorithms

• 第一著者: Peters, Jonas (auth)
• その他の著者: Janzing, Dominik (auth), Schölkopf, Bernhard (auth)
• フォーマット: 電子媒体 図書の章
• 言語: 英語
• 出版事項: Cambridge The MIT Press 2017
• シリーズ: Adaptive Computation and Machine Learning series
• 主題: 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
• オンライン･アクセス: OAPEN Library: download the publication; OAPEN Library: description of the publication

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...