How I Built a Multi-Agent System for Interrupted Time Series Analysis (ITSA)

Simply comparing metrics before and after a product change is often misleading, as it fails to account for pre-existing trends that can create the illusion of impact. Interrupted Time Series Analysis (ITSA) offers a more robust quasi-experimental method by modeling the pre-intervention data to project a counterfactual baseline. The true effect of an intervention is then measured as the difference between the actual observed results and this projected path of what would have happened anyway. To automate this complex statistical process, a multi-agent AI system can be built to run the analysis and perform diagnostic checks on its core assumptions.