Running summarizing tools on a new article

Abstract: Uplift modeling (also known as heterogeneous treatment effect modeling) aims to predict the incremental impact of a treatment (e.g. a marketing offer or medical intervention) on an individual's outcome. The R package maq provides tools for model evaluation in uplift modeling, including Qini curves for policy analysis with multiple treatment arms (GitHub-grf-labs/maq: Qini curves for multi-armed treatment rules) (Qini curves: Automatic cost-benefit analysis • grf). However, maq currently relies on traditional models (like trees or forests) to estimate uplift. In this paper, we propose a deep learning extension that enhances all major functionalities of maq-modeling, treatment effect estimation, and evaluation-using PyTorch. We introduce new neural network architectures for uplift modeling that handle multiple treatment arms and improve individual treatment effect estimation. The models are implemented in Python/PyTorch and can interface with R for evaluation (via maq R package) to leverage Qini-based metrics. Experiments on synthetic data and real-world datasets (marketing campaign and healthcare trials) demonstrate that our deep uplift models achieve better targeting performance and more accurate treatment effect estimates than traditional methods. For example, on a synthetic uplift dataset, our approach achieves a 30% lower Precision in Estimation of Heterogeneous Effect (PEHE) and a higher Qini coefficient than both a two-model logistic regression and a causal random forest. We also illustrate Qini curves and AUUC (Area Under the Uplift Curve) to compare model performance. Results show that the deep learning extensions can more effectively identify persuadable individuals and yield policies with higher incremental gains. These findings extend the maq framework with state-of-the-art deep learning, bridging R's policy evaluation tools with powerful neural estimators of uplift.