Causal Evaluation of Automated Provisioning through Interrupted Time-Series
DOI:
https://doi.org/10.66687/evbz9n64Keywords:
Machine Learning, Algorithms, Computer Simulation, Reproducibility of Results, Models, StatisticalAbstract
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. This study aims to estimate how workflow automation changes deployment delays and configuration mistakes while accounting for concurrent platform changes. The evaluation would follow a quasi-experimental interrupted time-series design based on dated infrastructure change logs. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast pre-intervention trends, post-intervention outcomes and contemporaneous negative-control tasks. The design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. Assessment will focus on provisioning time, post-deployment defect rates and uncertainty around estimated change points. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address seasonality, shifting request complexity, policy revisions and missing maintenance records. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].
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