Cause and Effect Business Analytics For Big and Small Data Chapman & Hall/CRC Computer Science & Data Analysis Series

Business analytics is the application of statistical and quantitative analysis, as well as formal modeling, to decision making. 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. The book is rooted in realistic and important cases used to illustrate the importance of thinking clearly about causality and applying the techniques of business analytics.

Introduction to causal business analytics. Review of common statistical/econometric and data mining techniques. Causal inference I. Causal inference II. Uplift (aka True-lift) analytics I. Uplift analytics II. Treatment optimization. Uplift analytics for non-random experiments. Causal analytics in time series I. Causal analytics in time series II. Structural Equation Modeling (SEM). Discussion and Summary.