Statistical Inference
Time Series Impact Analysis and Counterfactual Forecasting
Evaluation of whether an observed change in a metric was caused by an intervention or by trend, seasonality, or noise.
Dataset type: simulated. No confidential client or employer data.
Executive Summary
This case estimates whether a visible change in a time-dependent metric is consistent with an intervention effect after accounting for baseline trend, seasonality and noise.
Business Question
Did the intervention change the metric beyond what would have been expected under the pre-intervention trajectory?
Statistical Question / Hypothesis
The null hypothesis is that the post-intervention period follows the counterfactual path implied by pre-intervention behavior. The alternative is that the observed post-period departs from that expected path by a practically meaningful amount.
Dataset
The dataset is simulated as a weekly metric series with pre-period history, intervention date, trend, seasonality and comparison features. It is structured to separate intervention timing from recurring calendar effects.
Methodology
The analysis compares observed post-intervention values with a counterfactual forecast. It uses interrupted time series framing, forecast uncertainty and interval-based interpretation rather than relying on a simple before-after comparison.
Implementation
Python and R are used for decomposition, model diagnostics, counterfactual estimation and uncertainty intervals. Power BI is used only for executive reporting once the statistical outputs are stable.
Results
The output is an estimated cumulative impact, pointwise deviation from counterfactual and an uncertainty range. The result supports decisions only when the observed deviation is large enough to be both statistically and operationally relevant.
Limitations
The main limitations are unobserved concurrent events, limited pre-period length, structural breaks, model misspecification and overinterpretation when the intervention date is not sharply defined.
Executive Recommendation
Use the estimated counterfactual gap as directional evidence for rollout or investigation. When uncertainty remains wide, extend the observation window or add a matched comparison series.
Tools Used
Python, R and Power BI.
Links
Notebook, GitHub repository and executive PDF are coming soon.