Frank J. Fabozzi is Professor of Practice at Johns Hopkins Carey Business School. He is the author of Capital Markets, sixth edition; Entrepreneurial Finance and Accounting for High-Tech Companies; and Introduction to Fixed-Income Analysis and Portfolio Management and coauthor of Adaptive Finance; The Economics of FinTech; Foundations of Global Financial Markets and Institutions, fifth edition; and Simulation, Optimization, and Machine Learning for Finance, all published by the MIT Press.Sergio Focardi is Professor at the University of Genoa, where he teaches risk management and financial engineering. He has taught at EDHEC Business School at Stony Brook University, Princeton University, and Pôle Universitaire Léonard-de-Vinci at Paris la Défense. He is the author or coauthor of 24 books (including Adaptive Finance, published by the MIT Press) and more than 100 peer-reviewed papers.

ENDORSEMENTS“In Causal Modeling for Finance and Business, Frank J. Fabozzi and Sergio Focardi offer a much-needed exploration of one of the most important intellectual shifts in modern analytics: the move beyond correlation toward structured, intervention-based causal reasoning. They have clearly and rigorously integrated philosophy, probability, graph theory, structural causal models, and real-world financial applications into a single unified framework. In the process, they have provided researchers, investment professionals and policymakers alike with an indispensable, foundational understanding of decision-making under uncertainty.” —Andrew B. Weisman, Founding Partner, Market Revealed Preference

• ForewordPrefaceChapter 1: Beyond Correlation: Principles and Applications of Causal ModelsChapter 2: Causation And Correlation: Understanding Functional Relationships In Probabilistic SystemChapter 3: Probabilistic Frameworks For Causal InferenceChapter 4: Foundations Of Graph Theory For Causal ModelingChapter 5: Bayesian Networks, Causal Graphical Models, And Structural ModelsChapter 6: Learning Causal Structures And Equations: Methods And ChallengesChapter 7: Causal Reasoning And Causal InferenceChapter 8: The Practical Benefits Of Causal Modeling For Decision-MakingChapter 9: Practical Applications Of Causal Modeling In Business And InvestmentsChapter 10: The Process Of Deployment Of Causal ModelsChapter 11: Causal Models For Economics And Investment ManagementReference ListNotes

Frank J. Fabozzi is Professor of Practice at Johns Hopkins Carey Business School. He is the author of Capital Markets, sixth edition; Entrepreneurial Finance and Accounting for High-Tech Companies; and Introduction to Fixed-Income Analysis and Portfolio Management and coauthor of Adaptive Finance; The Economics of FinTech; Foundations of Global Financial Markets and Institutions, fifth edition; and Simulation, Optimization, and Machine Learning for Finance, all published by the MIT Press.Sergio Focardi is Professor at the University of Genoa, where he teaches risk management and financial engineering. He has taught at EDHEC Business School at Stony Brook University, Princeton University, and Pôle Universitaire Léonard-de-Vinci at Paris la Défense. He is the author or coauthor of 24 books (including Adaptive Finance, published by the MIT Press) and more than 100 peer-reviewed papers.

ENDORSEMENTS“In Causal Modeling for Finance and Business, Frank J. Fabozzi and Sergio Focardi offer a much-needed exploration of one of the most important intellectual shifts in modern analytics: the move beyond correlation toward structured, intervention-based causal reasoning. They have clearly and rigorously integrated philosophy, probability, graph theory, structural causal models, and real-world financial applications into a single unified framework. In the process, they have provided researchers, investment professionals and policymakers alike with an indispensable, foundational understanding of decision-making under uncertainty.” —Andrew B. Weisman, Founding Partner, Market Revealed Preference

• ForewordPrefaceChapter 1: Beyond Correlation: Principles and Applications of Causal ModelsChapter 2: Causation And Correlation: Understanding Functional Relationships In Probabilistic SystemChapter 3: Probabilistic Frameworks For Causal InferenceChapter 4: Foundations Of Graph Theory For Causal ModelingChapter 5: Bayesian Networks, Causal Graphical Models, And Structural ModelsChapter 6: Learning Causal Structures And Equations: Methods And ChallengesChapter 7: Causal Reasoning And Causal InferenceChapter 8: The Practical Benefits Of Causal Modeling For Decision-MakingChapter 9: Practical Applications Of Causal Modeling In Business And InvestmentsChapter 10: The Process Of Deployment Of Causal ModelsChapter 11: Causal Models For Economics And Investment ManagementReference ListNotes