Causal Inference in Statistics

Causal Inference in Statistics

A Primer

Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" However, despite the availability of hundreds of introductory texts on statistical methods of data analysis, until now, no beginner-level book has existed that addresses the growing array of methods for extracting causal information from data.

Causal Inference in Statistics fills that gap. Through simple examples and plain language, the book explains how to define causal parameters, the assumptions required to estimate them in various situations, how to express these assumptions mathematically, whether they have testable implications, how to predict the effects of interventions, and how to reason counterfactually. These are essential tools for any student of statistics aiming to use statistical methods to answer causal questions of interest.

This book is accessible to anyone interested in interpreting data, including undergraduates, professors, researchers, and even the curious layperson. Examples span a diverse range of fields, such as medicine, public policy, and law. A brief introduction to probability and statistics is provided for newcomers, and each chapter includes study questions to reinforce the reader's understanding.