Inference and Intervention
Causal Models for Business Analysis
Routledge – 2014 – 266 pages
Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which business elements to control or change – as the authors put it, a managerial intervention – must precede any decision on how to control or change them, and understanding causality is crucial to making effective interventions.
The authors cover the full spectrum of causal modeling techniques useful for the managerial role, whether for intervention, situational assessment, strategic decision-making, or forecasting. From the basic concepts and nomenclature of causal modeling to decision tree analysis, qualitative methods, and quantitative modeling tools, this book offers a toolbox for MBA students and business professionals to make successful decisions in a managerial setting.
"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
@Contents: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
Michael D. Ryall is an Associate Professor of Strategy at the University of Toronto. He holds a PhD in economics from the University of California, Los Angeles and an MBA from the University of Chicago. Ryall is President of the Strategy Research Initiative, a scholarly society dedicated to the advancement of research in the field of management. His primary research interest is the game-theoretic foundations of business strategy and his work has been published in leading international journals. Ryall 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 received a PhD from the University of Michigan in 2012 in a joint program with the departments of political science and philosophy, as well as earning UM's graduate certificate in complexity in 2008. Aaron holds an MS in mathematics from Northeastern University, as well as a BS in economics and a BA in philosophy from the University of Florida. Aaron's research specialty is complexity science, methodology for modeling complex systems, and measuring dynamics in large datasets. He is currently a researcher at the RIKEN Brain Science Institute in Japan. Previously he worked as a research fellow in the Rotman School of Management at the University of Toronto, as a software engineer at Lockheed Martin Corporation, and has taught numerous workshops on complexity, networks, and agent-based modeling around the world.