A Feature Model-Based Configurable Framework for Reproducible Intervention Evaluation
Year: 2026
Type: article
Abstract: Intervention evaluation frequently relies on fixed analytical workflows that provide limited support for methodological adaptability, reproducibility, and systematic management of analytical variability. This study proposes a Feature Model-based configurable framework that represents data preparation, feature construction, statistical inference, robustness validation, and decision-support generation as interdependent analytical features. Mandatory, optional, alternative, and dependency-constrained decisions are modeled explicitly, enabling the systematic generation, validation, and diagnosis of reproducible analytical configurations. The framework was instantiated in a managerial capability development intervention involving micro-enterprises. Across the strategic, financial, and operational capability dimensions, the generated pipelines identified statistically significant improvements with Cohen’s d values ranging from 0.48 to 0.65. The direction and inferential interpretation of the intervention effects remained stable across the admissible preprocessing, imputation, and testing configurations evaluated in the case study. Because the empirical assessment uses a single dataset from one application domain, the findings establish technical feasibility and within-case robustness rather than cross-domain generalizability. The study contributes a variability-aware analytical architecture and an open-source Python-based engine integrating Feature Model reasoning, configuration validation, and Parallel FastDiag-based conflict diagnosis, thereby supporting transparent, inspectable, and adaptable intervention-evaluation workflows.
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7204705994