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ADIF: A Decision Intelligence Framework Integrating Predictive Driver Discovery, Causal Discovery, Causal Inference, Counterfactual Reasoning, and LLM-Assisted Decision Support for Retail Analytics
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 149 |
| Year of Publication: 2026 |
| Authors: Greeshma Chanduri, Sampath Kokkonda, Moin Bhuiyan |
10.5120/ijca66d577e28e5c
|
Greeshma Chanduri, Sampath Kokkonda, Moin Bhuiyan . ADIF: A Decision Intelligence Framework Integrating Predictive Driver Discovery, Causal Discovery, Causal Inference, Counterfactual Reasoning, and LLM-Assisted Decision Support for Retail Analytics. International Journal of Computer Applications. 187, 149 ( Oct 2026), 26-32. DOI=10.5120/ijca66d577e28e5c
Abstract
Retail organizations increasingly require analytical systems that can move beyond prediction toward explanations of observed business changes and structured decision support. This paper presents ADIF, an end-to-end decision intelligence framework that connects predictive modeling, predictive-driver discovery, causal analysis, counterfactual reasoning, and large language model (LLM)-assisted communication in a single workflow. The framework combines XGBoost and SHAP for predictive modeling and feature attribution, the PC algorithm for causal-structure screening, DoWhy for treatment-effect estimation and refutation, counterfactual analysis for scenario evaluation, and Llama 3.1 through Ollama for natural-language decision support. ADIF was evaluated on the Online Retail II and Instacart datasets. On Online Retail II, Order Change and Quantity Change represented 63.42% and 36.58%, respectively, of the reported normalized feature-attribution contribution. The estimated effect of Quantity Change on Revenue Change was 0.59748, with a reported 95% confidence interval of approximately [0.5721, 0.6229]. On Instacart, the estimated effect of Orders on Reorder Rate was 0.006417, with a reported 95% confidence interval of approximately [0.00591, 0.00693]. Predictive evaluation produced a mean cross-validated R2 of 0.5883 for XGBoost on Instacart. The counterfactual stage was configured for 5%, 10%, 20%, and 50% treatment-change scenarios as model-based what-if analyses; no scenario outcome is claimed as a verified finding. Refutation experiments produced a negligible change under the random-common-cause test and a near-zero estimate under placebo treatment; these are treated as sensitivity checks rather than proof of causality. The LLM layer converts structured quantitative findings and limitations into decision-support narratives. The framework’s contribution is the explicit evidence handoff between analytical stages while keeping predictive attribution, causal estimation, counterfactual scenarios, and language generation conceptually separate.
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