To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates

DOI10.1016/J.EJOR.2022.03.049OpenAlexW4220967455MaRDI QIDQ2098058

Jente Van Belle, Diego Olaya, Marie-Anne Guerry, Wouter Verbeke

Publication date: 17 November 2022

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2022.03.049

zbMATH Keywords

rankinganalyticsexpected profitclassification boundarycausal classification

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Uses Software

• XGBoost
• grf
• CausalML

Cites Work

• Unnamed Item
• Generalized random forests
• Bayesian inference for causal effects: The role of randomization
• Instance-dependent cost-sensitive learning for detecting transfer fraud
• Targeting customers under response-dependent costs
• Response transformation and profit decomposition for revenue uplift modeling
• The central role of the propensity score in observational studies for causal effects
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence

This page was built for publication: To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates