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, 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.
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
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