14 Mar “Causal Machine Learning for Business Decision-Making” by dr. Wouter Verbeke
March 30th 2023, 12:15, room S.R42.4.502 (Solbosch)
Machine learning is a powerful tool to support business decision-making. For instance, predictive models can be learned from data to anticipate the future and to make informed decisions, with the eventual objective of optimizing the efficiency and effectiveness of business operations. Even better than having predictive models, which tell you what will happen, is to have prescriptive models, which tell you what to do so as to optimize the outcome of interest. To this end, in the field of prescriptive analytics and operations research, simulation models are developed by an human expert modeler in the form of a series of mathematical equations. As an alternative approach, causal machine learning can be adopted to learn to predict the future as a function of the decisions that are made. In other words, causal machine learning models estimate the net effect on the outcome(s) of interest that would be caused by various potential business decisions. As such, these models may directly indicate the optimal decision. In this talk, I will demonstrate the use and need for causal machine learning by discussing on a number business cases. I will discuss on the challenges in estimating causal effects and learning a simulation model from data, and introduce some basic causal machine learning methods.
For more information, please contact John Iacono (email@example.com)