**Elements of Causal Inference: Foundations and Learning Algorithms**

by J. Peters, D. Janzing, B. Schölkopf

**Publisher**: The MIT Press 2017**ISBN-13**: 9780262037310**Number of pages**: 289

**Description**:

This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems.

Download or read it online for free here:

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(21MB, PDF)

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