Dominique Haughton (PhD MIT 1983) is Professor Emerita of Mathematical Sciences and Global Studies at Bentley University near Boston, and Affiliated Researcher at Université Paris 1 (Pantheon-Sorbonne, SAMM) and at Université Toulouse 1 (TSE-R). Her widely published work concentrates on how to best leverage modern analytics techniques to address questions of business or societal interest. She is an alumna of the Ecole Normale Supérieure and a Fellow of the American Statistical Association.Jonathan Haughton earned his PhD in economics from Harvard University in 1983. He has published widely in the areas of economic development, taxation, the environment, and the analysis and measurement of poverty. Until recently, he chaired the economics department at Suffolk University, Boston, and he has taught or worked as a consultant in over 20 countries on five continents.Victor S.Y. Lo is an executive with over three decades of consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Marketing, Risk Management, Financial Econometrics, Insurance, Product Development, Transportation, Healthcare, Operations Management, and Human Resources, and is a pioneer of uplift modeling. He is currently SVP, Data Science and AI at Fidelity Investments, and has led data science and analytics teams in various organizations. Victor earned a master’s degree in Operational Research and a PhD in Statistics, and was a Postdoctoral Fellow in Management Science.

• 1. Introduction to Cause-and-Effect Business Analytics 2. Review of common data mining techniques 3. Causality 4. Causality: Synthetic Control, Regression Discontinuity, and Instrumental Variables 5. Directed Acyclic Graphs 6. Uplift Analytics I: Mining for the Truly Responsive Customers and Prospects 7. Test and Learn for Uplift 8. Uplift Analytics III: Model-Driven Decision-Making and Treatment Optimization Using Prescriptive Analytics 9. Uplift Analytics IV: Advanced Modeling Techniques for Randomized and Non-Randomized Experiments 10. Causality in Times Series Data 11. Structural Equation Models 12. Discussion and Summary

"The book is valuable for data scientists and business strategists for actual applications of the causal modeling. It also is helpful for instructors and students for courses on the capabilities of modern statistical causality modeling explained with multiple examples on business data."Stan Lipovetsky, Technometrics, 2026.

Dominique Haughton (PhD MIT 1983) is Professor Emerita of Mathematical Sciences and Global Studies at Bentley University near Boston, and Affiliated Researcher at Université Paris 1 (Pantheon-Sorbonne, SAMM) and at Université Toulouse 1 (TSE-R). Her widely published work concentrates on how to best leverage modern analytics techniques to address questions of business or societal interest. She is an alumna of the Ecole Normale Supérieure and a Fellow of the American Statistical Association.Jonathan Haughton earned his PhD in economics from Harvard University in 1983. He has published widely in the areas of economic development, taxation, the environment, and the analysis and measurement of poverty. Until recently, he chaired the economics department at Suffolk University, Boston, and he has taught or worked as a consultant in over 20 countries on five continents.Victor S.Y. Lo is an executive with over three decades of consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Marketing, Risk Management, Financial Econometrics, Insurance, Product Development, Transportation, Healthcare, Operations Management, and Human Resources, and is a pioneer of uplift modeling. He is currently SVP, Data Science and AI at Fidelity Investments, and has led data science and analytics teams in various organizations. Victor earned a master’s degree in Operational Research and a PhD in Statistics, and was a Postdoctoral Fellow in Management Science.

• 1. Introduction to Cause-and-Effect Business Analytics 2. Review of common data mining techniques 3. Causality 4. Causality: Synthetic Control, Regression Discontinuity, and Instrumental Variables 5. Directed Acyclic Graphs 6. Uplift Analytics I: Mining for the Truly Responsive Customers and Prospects 7. Test and Learn for Uplift 8. Uplift Analytics III: Model-Driven Decision-Making and Treatment Optimization Using Prescriptive Analytics 9. Uplift Analytics IV: Advanced Modeling Techniques for Randomized and Non-Randomized Experiments 10. Causality in Times Series Data 11. Structural Equation Models 12. Discussion and Summary

"The book is valuable for data scientists and business strategists for actual applications of the causal modeling. It also is helpful for instructors and students for courses on the capabilities of modern statistical causality modeling explained with multiple examples on business data."Stan Lipovetsky, Technometrics, 2026.

Dominique Haughton (PhD MIT 1983) is Professor Emerita of Mathematical Sciences and Global Studies at Bentley University near Boston, and Affiliated Researcher at Université Paris 1 (Pantheon-Sorbonne, SAMM) and at Université Toulouse 1 (TSE-R). Her widely published work concentrates on how to best leverage modern analytics techniques to address questions of business or societal interest. She is an alumna of the Ecole Normale Supérieure and a Fellow of the American Statistical Association.Jonathan Haughton earned his PhD in economics from Harvard University in 1983. He has published widely in the areas of economic development, taxation, the environment, and the analysis and measurement of poverty. Until recently, he chaired the economics department at Suffolk University, Boston, and he has taught or worked as a consultant in over 20 countries on five continents.Victor S.Y. Lo is an executive with over three decades of consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Marketing, Risk Management, Financial Econometrics, Insurance, Product Development, Transportation, Healthcare, Operations Management, and Human Resources, and is a pioneer of uplift modeling. He is currently SVP, Data Science and AI at Fidelity Investments, and has led data science and analytics teams in various organizations. Victor earned a master’s degree in Operational Research and a PhD in Statistics, and was a Postdoctoral Fellow in Management Science.

• 1. Introduction to Cause-and-Effect Business Analytics 2. Review of common data mining techniques 3. Causality 4. Causality: Synthetic Control, Regression Discontinuity, and Instrumental Variables 5. Directed Acyclic Graphs 6. Uplift Analytics I: Mining for the Truly Responsive Customers and Prospects 7. Test and Learn for Uplift 8. Uplift Analytics III: Model-Driven Decision-Making and Treatment Optimization Using Prescriptive Analytics 9. Uplift Analytics IV: Advanced Modeling Techniques for Randomized and Non-Randomized Experiments 10. Causality in Times Series Data 11. Structural Equation Models 12. Discussion and Summary

"The book is valuable for data scientists and business strategists for actual applications of the causal modeling. It also is helpful for instructors and students for courses on the capabilities of modern statistical causality modeling explained with multiple examples on business data."Stan Lipovetsky, Technometrics, 2026.