Short description:

This book examines under what circumstances, and with which techniques, one can reasonably infer cause and effect in a business setting, and use the insight to drive business decisions. It is written at a level accessible to anyone with a master?s degree in analytics, business, economics, statistics, computer science, or a related field.

Long description:

Among the most important questions that businesses ask are some very simple ones: If I decide to do something, will it work? And if so, how large are the effects? To answer these predictive questions, and later base decisions on them, we need to establish causal relationships.

Establishing and measuring causality can be difficult. This book explains the most useful techniques for discerning causality, and illustrates the principles with numerous examples from business. It discusses randomized experiments (aka A/B testing), and techniques such as propensity score matching, synthetic controls, double differences, and instrumental variables. There is a chapter on the powerful AI approach of Directed Acyclic Graphs (aka Bayesian Networks), another on structural equation models, and on time-series techniques, including Granger causality.

At the heart of the book are four chapters on uplift modelling, where the goal is to help firms determine how best to deploy their resources for marketing or other interventions. We start by modelling uplift, discuss the test-and-learn process, and provide an overview of the prescriptive analytics of uplift.

The book is written in an accessible style, and will be of interest to data analysts and strategists in business, to students and instructors of business and analytics who have a solid foundation in statistics, and to data scientists who recognize the need to take seriously the need for causality as an essential input into effective decision making.

Table of Contents:

1. Introduction to Cause-and-Effect Business Analytics   2. Review of common data mining techniques   3. Causality   4. Causality: Synthetic Control, Regression Discontinuity, and Instrumental Variables   5. Directed Acyclic Graphs   6. Uplift Analytics I: Mining for the Truly Responsive Customers and Prospects   7. Test and Learn for Uplift   8. Uplift Analytics III: Model-Driven Decision Making and Treatment Optimization Using Prescriptive Analytics   9. Uplift Analytics IV: Advanced Modeling Techniques for Randomized and Non-Randomized Experiments   10. Causality in Times Series Data   11. Structural Equation Models   12. Discussion and Summary