Inference and Intervention ─ Causal Models for Business Analysis

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.

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.