Elements of Causal Inference: Foundations and Learning AlgorithmsA 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-contained and concise introduction to causal models and how to learn them from data. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts. |
Contents
Statistical and Causal Models | 1 |
Assumptions for Causal Inference | 15 |
CauseEffect Models | 33 |
Learning CauseEffect Models | 43 |
Connections to Machine Learning I | 71 |
Multivariate Causal Models | 81 |
Learning Multivariate Causal Models | 135 |
Connections to Machine Learning II | 157 |