Essential Data Science for Business: Causal Inference and Uplift Modeling

November 18, 2020 1-4 ET

The tutorials in this NISS series involve the Top 10 analytics approaches of the key topics that are used in business today!  Students and faculty, these are perhaps the top ten most important and practical topics that may not be covered in your program of study.  (Review the Overview Presentation about all 10 Sessions).

Causal Inference and Uplift Modeling


In this tutorial, the concept of Uplift will be introduced.  We will review existing methods, contrast with the traditional approach, and introduce a new method that can be implemented with standard software. A method and metrics for model assessment will be recommended. Our discussion will include new approaches to handling a general situation where only observational data are available, i.e. without randomized experiments, using techniques from causal inference. Additionally, an integrated modeling approach for uplift and direct response (where it can be identified who actually responded, e.g., click-through or coupon scanning) will be discussed. Last but not least, extension to the multiple treatment situation with solutions to optimizing treatments at the individual level will also be discussed. While the tutorial is geared towards marketing applications (“personalized marketing”), the same methodologies can be readily applied in other fields such as insurance, medicine, education, political, and social programs. Examples from the retail and non-profit industries will be used to illustrate the methodologies.


Victor Lo (Fidelity Investments), Dominique Haughton (Bentley University) and Jonathan Haughton (Suffolk University)


NISS is interested in sharing knowledge.  To this end, these webinars have been geared to provide practical information that you can use tomorrow. Examples, projects and code sharing are a part of these sessions wherever possible.

Series Prerequisites

Participants require a working knowledge of probability distributions, statistical inference, statistical modeling and time series analysis as a prerequisite. Students who do not have this foundation or have not reviewed this material within the past couple of years will struggle with the concepts and methods that build on this foundation.


Select a registration/payment option above the 'Register for this Event' button ($35 for this Data Science Essentials tutorial session, $250 for all 10 Essential Data Science for Business tutorial sessions.  Can attend this live session? Post Session Access to tutorial materials and recording can be obtained for $35 after the event is over.). NISS Affiliates, (, please send an email to  Notifications: You will recieve an email that comes immediately to let you know you paid.  Links to the event will come via email the day before and one hour prior to the actual session.


About the Instructors

Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over 25 years of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance Analytics, Product Development, Healthcare Analytics, Operations Management, Transportation, and Human Resources. He is actively engaged with causal inference and is a pioneer of Uplift/True-lift modeling, a key subfield of data science.

Dominique Haughton (Ph.D. MIT 1983) is Professor of Mathematical Sciences at Bentley University in Waltham, Massachusetts, near Boston, and Affiliated Researcher at the Université Toulouse 1, France.  Her major areas of interest are applied statistics, statistics and marketing, the analysis of living standards surveys, data mining, and model selection. She is the editor-in-chief of Case Studies in Business, Industry and Government Statistics (CSBIGS), and has published over fifty articles in scholarly journals, including The American Statistician, Annals of Statistics, Sankhya, Communications in Statistics, and Statistica Sinica. Dominique is a Fellow of the American Statistical Association. 

Jonathan Haughton (Ph.D. Harvard 1983) is Professor of Economics at Suffolk University, and Senior Economist at the Beacon Hill Institute for Public Policy, both in Boston. A specialist in the areas of economic development, international trade, and taxation, and a prize-winning teacher, he has lectured, taught, or conducted research in over a score of countries on five continents. His Handbook on Poverty and Inequality (with Shahidur Khandker) was published by the World Bank in 2009, his articles have appeared in over 30 scholarly journals, and he has written numerous book chapters and over a hundred reports.

Event Type


National Institute of Statistical Sciences


National Institute of Statistical Sciences


$35 for this session; $250 for all 10 Data Science Sessions


Online Tutorial
Instructors: Victor Lo (Fidelity Investments), Dominique Haughton (Bentley University) and Jonathan Haughton (Suffolk University)