1st Edition
Inference and Intervention Causal Models for Business Analysis
1. Introduction to Causal Analysis 2. Qualitative Causal Models 3. Application: Interview Case Study 4. Quantitative Causal Models 5. Situational Analysis 6. Application: Modeling Business Financials 7. Single-Agent Interventions 8. Application: Disrupting the Taxi Business 9. Multi-Agent Intervention 10. Data-Driven Causal Modeling
Biography
Michael D. Ryall is an Associate Professor of Strategy at the University of Toronto, Canada. He teaches courses on advanced strategy analysis and on causal modeling to undergraduate, MBA and EMBA students. Prior to obtaining a PhD and becoming a full-time scholar, he held positions in consulting, general management and finance.
Aaron L. Bramson is currently a researcher at the RIKEN Brain Science Institute, Japan. Previously he worked as a research fellow in the Rotman School of Management at the University of Toronto, Canada, as a software engineer at Lockheed Martin Corporation, and has taught numerous workshops on complexity, networks, and agent-based modeling around the world.
"One of the most difficult problems any real world decision maker faces is how to properly incorporate prior information into current decisions. Modes of analysis used by management consultants such as issue trees direct our thinking away from this key question of causality. This stunning book by Ryall and Bramson introduces causal models as a method of focusing our attention on what is important: Why are these things happening, and (therefore) what can we do about it? Both modeling and strategy are given full attention. Although this book is designed for managers and is full of practical managerial examples, it is essential reading for anyone who has to make significant decisions."
- David K. Levine, Washington University in St. Louis
"Ryall and Bramson have written a remarkable book that combines a clear, comprehensive introduction into qualitative and quantitative causal models with case studies and examples that show managers how to apply causal models to see the world more clearly and make better decisions. After reading this book, you'll quit your spreadsheet and start drawing causal networks instead."
- Scott E. Page, University of Michigan and Santa Fe Institute
