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:
Download link
(21MB, PDF)
Similar books
The Future of Machine Intelligenceby David Beyer - O'Reilly Media
The series of interviews in this exclusive report unpack concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.
(1653 views)
A Brief Introduction to Machine Learning for Engineersby Osvaldo Simeone - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(714 views)
An Introductory Study on Time Series Modeling and Forecastingby Ratnadip Adhikari, R. K. Agrawal - arXiv
This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.
(5776 views)
An Introduction to Probabilistic Programmingby Jan-Willem van de Meent, et al. - arXiv.org
This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.
(458 views)