Abstract: Abstract: When making decisions that impact a specific context, it is essential to include a causal effect estimation analysis. This analysis allows us to compare potential outcomes under different treatment options or the control, aiding in selecting the best treatment option that leads to optimal results. However, merely estimating individual treatment effects may not suffice for making truly optimal decisions. To reveal this limitation, our study explores the incorporation of additional criteria, such as the uncertainty of these estimations, measured through a concept similar to the Conditional Value-at-Risk, commonly used in portfolio and insurance management. Additionally, we evaluate the inclusion of a specific prediction condition, particularly when the highest output is the desired outcome. With these intentions in mind, we propose a comprehensive methodology for multi-treatment selection, especially in situations where greater output is more desirable. Our suggested approach ensures the satisfaction of the overlap condition for comparing outcomes for treated and control groups. This involves training propensity score models as a preliminary step before employing traditional causal models — a crucial aspect often overlooked in other research. To illustrate the practical application of our methodology, we focus on the significant problem of credit card limit adjustment, which has historically been reliant on expert-driven choices. Analyzing historical data from a fintech company, we discovered that relying solely on counterfactual predictions is inadequate for generating appropriate credit line modifications. Instead, incorporating the two additional criteria significantly enhances the performance of the generated policy.

Short bio: Dr. Cristián Bravo is an Associate Professor and Canada Research Chair in Banking and Insurance Analytics at Western University, and Director of the Banking Analytics Lab. His research focuses on data science, analytics, and credit risk, particularly exploring multimodal deep learning, causal inference, and social network analysis to understand consumer-financial institution relations. With over 75 publications in prestigious journals and conferences, he contributes significantly to operational research, finance, and computer science. He frequently appears on CBC News’ Weekend Business Panel, and has been quoted by The Wall Street Journal, WIRED, CTV, The Toronto Star, The Globe and Mail, and Global News discussing topics in Banking, Finance, and Artificial Intelligence.