“To Explain or To Predict?” by Professor Galit SHMUELI
Professor Galit SHMUELI
Tsing Hua Distinguished Professor of Business Analytics, Institute of Service Science
Director of the Center for Service Innovation & Analytics
College of Technology Management
National Tsing Hua University
Empirical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines, there is near-exclusive use of empirical modeling for causal explanation with the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge and for proper use in practice. While this distinction has been recognized in the philosophy of science, the statistical and data mining literature lack a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. In this talk I will clarify the distinction between explanatory and predictive modeling and reveal the practical implications in terms of data analysis. I will also describe how predictive modeling can be useful for advancing theory, in the context of scientific research.