The scientific process is all about finding causal relationships, as is evidence-based medicine. This book shows you how to establish this kind of causal relationships.

What does this book cover?

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction is very well organised. It gives you all of the information you need to answer the kind of questions the biomedical and social sciences were built upon: “what would happen if we changed people’s situations in a particular way?” or “How can we prevent this pathology?”

Other important features of Causal Inference for Statistics include:

• An exploration of leading analysis methods, such as instrumental variables, propensity-scoring and data matching
• A guide to experimental randomisation in observational studies
• Thoughts on the fundamental problem of causal inference – the ability to observe only one outcome per subject

About the Author

Donald B. Rubin is a department chair and professor of statistics at Harvard University. He has nearly 40 years of teaching experience, and has nearly 400 publication credits to his name. Guido W. Imbens teaches at Stanford University’s Graduate School of Business. He has been published by journals like Biometrika, Econometrica and the Annals of Statistics.

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction is a guide to a complex yet vital topic.