Cause and Effect Business Analytics
For Big and Small Data

Chapman & Hall/CRC Computer Science & Data Analysis Series

Authors:

Language: Anglais
Publication date:
· 15.6x23.5 cm · Hardback

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.

Ideal for analytics or data sciences practitioners; graduate students and academic researchers in the field of business analytics/data mining/machine learning and applied statistics.