ITSA and why it matters A brief introduction Measurement in digital business has long trusted the pre- versus post-intervention approach for exactly these situations. I'm sure you've heard something like: 'just compare what happened after the change with the period before.' Maybe you've even believed it's a nice approach. Respectfully, let me show you why it falls short.

Figure 1. Line chart of daily orders rising steadily; a naive pre/post comparison around a new checkout launch shows a +10% lift that is just the trend. Image by author.

Simulated daily orders climb steadily from about 100 to 140 over 130 days. A new checkout ships on day 80, but the series shows no break: the upward trend simply continues. Comparing the pre-period average (days 40–80) with the post-period average (days 80–120) suggests a +10% lift that the checkout didn't cause; the pre-existing trend did.

This is the naive pre/post trap that Interrupted Time Series Analysis (ITSA) is designed to avoid. From a statistical point of view, comparing the mean before and after the intervention is a naive approach: it ignores pre-existing trends and seasonality, does not account for autocorrelation when quantifying uncertainty, and cannot distinguish the intervention from concurrent shocks. In the Figure 1 scenario, this comparison would lead you to conclude that the intervention increased daily orders by 10%.

And you’d be wrong. Figure 2. Same daily orders series with the pre-launch trend projected forward as a counterfactual; observed orders stay on that projection, so the effect is zero.

Image by author. T...