Introducing RobustiPy: An efficient next generation multiversal library with model selection, averaging, resampling, and explainable artificial intelligence

[2506.19958] RobustiPy: An efficient next generation multiversal library with model selection, averaging, resampling, and explainable artificial intelligence Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2506.19958 (stat) [Submitted on 24 Jun 2025 ( v1 ), last revised 18 May 2026 (this version, v4)] Title: RobustiPy: An efficient next generation multiversal library with model selection, averaging, resampling, and explainable artificial intelligence Authors: Daniel Valdenegro , Jiani Yan , Duiyi Dai , Charles Rahal View a PDF of the paper titled RobustiPy: An efficient next generation multiversal library with model selection, averaging, resampling, and explainable artificial intelligence, by Daniel Valdenegro and 2 other authors View PDF HTML (experimental) Abstract: Scientific inference is often undermined by the vast but rarely explored "multiverse" of defensible modelling choices, which can generate results as variable as the phenomena under study. We introduce RobustiPy, an open-source Python library that systematizes multiverse analysis and model-uncertainty quantification at scale. RobustiPy unifies bootstrap-based inference, combinatorial specification search, model selection and averaging, joint-inference routines, and explainable AI methods within a modular, reproducible framework. Beyond exhaustive specification curves, it supports rigorous out-of-sample validation and quantifies the marginal contribution of each covariate. We demonstrate its utility across five simulation designs and ten empirical case studies spanning economics, sociology, psychology, and medicine, including a re-analysis of widely cited findings with documented discrepancies. Benchmarking on ~672 million simulated regressions shows that RobustiPy delivers state-of-the-art computational efficiency while expanding transparency in empirical research. By standardizing and accelerating robustness analysis, RobustiPy transforms how researchers interrogate sensitivity across the analytical multiverse, offering a practical foundation for more reproducible and interpretable computational science. Subjects: Methodology (stat.ME) ; General Economics (econ.GN); Applications (stat.AP); Computation (stat.CO) Cite as: arXiv:2506.19958 [stat.ME] (or arXiv:2506.19958v4 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2506.19958 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Charles Rahal [ view email ] [v1] Tue, 24 Jun 2025 19:11:42 UTC (925 KB) [v2] Wed, 10 Sep 2025 17:13:59 UTC (892 KB) [v3] Fri, 10 Apr 2026 12:57:41 UTC (989 KB) [v4] Mon, 18 May 2026 18:14:02 UTC (1,017 KB) Full-text links: Access Paper: View a PDF of the paper titled RobustiPy: An efficient next generation multiversal library with model selection, averaging, resampling, and explainable artificial intelligence, by Daniel Valdenegro and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2025-06 Change to browse by: econ econ.GN q-fin q-fin.EC stat stat.AP stat.CO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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