Published Online:July 2026
Product Name:The IUP Journal of Computer Sciences
Product Type:Article
Product Code:IJCS030726
DOI:10.71329/IUPJCS/2026.20.3.31-42
Author Name:Kaushik Bar
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:31-42
The paper proposes a Bayesian causal forest (BCF) framework to estimate the heterogeneous treatment effects (HTEs) of promotions, enabling counterfactual demand forecasting that is both personalized and uncertainty-aware. Unlike deterministic machine learning (ML) models or fixed-effects regressions, the approach leverages a Bayesian treatment of uncertainty, allowing for credible interval estimation around individual uplift predictions. The model is benchmarked using three publicly available datasets (RetailHero sales uplift dataset, Rossmann store sales dataset, and Criteo uplift prediction dataset) against widely used baselines, including S-learner, T-learner, X-learner, causal forests, and standard demand forecasting models. The results show that the proposed BCF model consistently outperforms existing methods in uplift accuracy, Qini coefficient, and uncertainty calibration error, while also providing interpretable insights into segment-level responsiveness to marketing interventions. This study demonstrates the feasibility and value of integrating Bayesian causal inference into business analytics pipelines for targeted promotional optimization.
In retail, promotional campaigns are pivotal levers for boosting short-term sales and influencing long-term customer behavior.