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This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the
Elements of Causal Inference
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Authors: Jonas Peters
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Type: BOOK - Published: 2017-11-29 - Publisher: MIT Press

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A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is
An Introduction to Causal Inference
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This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical
Cause Effect Pairs in Machine Learning
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This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause
Essays in Honor of Cheng Hsiao
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Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of