Cause, Effect, and Everything in Between An Introduction to Causal Inference

A practical guide to understanding the science of cause-and-effect for everyday decision-making.

In Cause, Effect, and Everything in Between, Aboozar Hadavand provides an easy-to-read and non-technical foundation to causal inference, especially for readers without a strong background in math and statistics. Rather than using statistical equations and mathematical theory, Hadavand focuses on developing readers' ability to analyze causal questions through a causal perspective. Using relatable examples, including the myth of the Swimmer's Body Illusion, the relationship between sleep apnea and growing a beard, and the relationship between smoking and dementia, Hadavand simplifies complex causal ideas.

The book starts by defining the fundamental concepts of causality, such as causal questions, causes, and effects. It then explores different types of causal inference problems, graphical tools for expressing causality, the shortcomings of randomized trials, and methods for inferring causality from observational data. Further, Hadavand debunks common misconceptions and teaches readers to differentiate between correlation and causation at a deep level by simplifying the concept of confounding bias and causal graphs. A concise and accessible introduction to causal inference that also includes end-of-chapter case studies with answers, this book equips readers to understand and critique scientific findings involving causal claims.

Aboozar Hadavand is Professor of Computational Sciences at Minerva University. For the past ten years, he has taught statistics, causal inference, and their applications in the social sciences, especially economics, at Barnard College of Columbia University, Brooklyn College, and Minerva University. He is the co-founder of the website Cauzl, which aims to teach causal inference to undergraduate students. His research in economics and causal inference has been published in journals such as the Journal of Economic Literature (JEL) and the Journal of the American Medical Association (JAMA).

Preface
1. What is causality?
2. The causal framework
3. Causal graphs and causal paths
4. Causal inference using interventional data
5. Causal inference using observational data
6. Quasi-experimental methods
7. A framework for evaluating causal studies
8. Causal case studies

Answers to end of chapter questions