Causal Evaluation of Automated Provisioning through Interrupted Time-Series: A Cross-Site Validation Agenda

Abstract

Faster provisioning after an automation rollout does not establish that the automation caused the improvement. This gap has consequences for interpreting performance outside carefully curated evaluation settings. The research question is whether it is possible to estimate how workflow automation changes deployment delays and configuration mistakes while accounting for concurrent platform changes. Previous work provides context for sampling, input validation and study boundaries [3–7]. The planned analysis uses a quasi-experimental interrupted time-series design based on dated infrastructure change logs. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. Under matched evaluation conditions, the protocol compares pre-intervention trends, post-intervention outcomes and contemporaneous negative-control tasks. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. Performance will be judged using provisioning time, post-deployment defect rates and uncertainty around estimated change points. Interpretation will account for seasonality, shifting request complexity, policy revisions and missing maintenance records. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].