Social Impact Manager, Consultant and Trainer | Data Science and Impact Assessment | PhD in Distance Education
🔄 📊This study by Jannik Rößler and Detlef Schoder highlights the importance of choosing the proper method to predict the incremental effect of a treatment, especially in areas such as churn management and patient care. Comparing 15 "uplift modeling" and "heterogeneous treatment effects" (HTE) methods on synthetic and real-world datasets, the results reveal more robust and effective methods, including "Uplift-CHI" (U-CHI), " Uplift-Kullback-Leibler" (U-KL), "Bayesian Causal Forest" (BCF) and "Contextual Treatment Selection" (CTS). The conclusion emphasizes the importance of evaluating methods from both areas to optimize segmentation policies. 🔍 Methods compared include CHAID decision trees, Two-Model Approach, S-learner, Lai's Generalization, X-learner, Treatment Dummy Approach, R-learner, Uplift Random Forest, Contextual Treatment Selection, Interaction Tree, Causal Inference Tree, Generalized Random Forest and Bayesian Causal Forest. The analysis highlights the variable performance of these methods across different data sets, highlighting the need to consider both approaches to evaluate the incremental effect of treatment. The results indicate that improving segmentation policies requires the evaluation of several approaches from both research areas. #machinelearning #ML #research #data #datascientists #datascience #causality #causalinference #causalml #uplift #upliftmodeling #HTE #HeterogeneousTreatmentEffects #artificialintelligence #causalAI #econometrics
Co-Founder @ Pixit | PhD | Machine Learning Scientist | MIT
11moThanks for sharing Nuno Lopes. I find it intriguing that the performance of the different methods differs so much depending on the dataset. That doesn't make using uplift modeling easy - but it can still be worth it!