Upload a CSV with a date and a metric, set the intervention date, and get a segmented regression: the immediate jump, the trend break, and the gap versus the old trend — each with a confidence interval. Free.
Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size — sign up to analyze your full dataset.
Splitting the series at the intervention...
Sent to — the series with fitted segments and counterfactual projection, level and slope changes with confidence intervals, the counterfactual gap, the autocorrelation check, R code, and AI insights.
Analyze another fileThe analysis orders your series in time, splits it at the intervention date (provided, or assumed at the series midpoint and flagged), and fits a segmented OLS regression of the value on time, a post-intervention indicator, and time-since-intervention. The indicator's coefficient is the LEVEL change — the immediate jump at the break — and the time-since coefficient is the SLOPE change — the bend in the trend; each is reported with a 95% confidence interval and p-value. The pre-intervention trend is then extended forward as a counterfactual, and the gap between it and the fitted series at the end of the data is reported in units and percent. A lag-1 residual autocorrelation check warns when classical standard errors understate the uncertainty.
Use it when something happened at a known point in time — a policy, a launch, a price change — and you have a single metric measured regularly before and after, with no untouched control group to compare against.
Not for changes with a control group (use the difference-in-differences tool — a control is stronger evidence), not for series shorter than 8 points on either side of the break, and not a substitute for a randomized experiment when you can run one.
Built for: Analysts, operations and policy teams evaluating a change against a single metric's history
Typical data source: Any spreadsheet or CSV with a date column and a numeric metric measured regularly, and a known intervention date
One row per period with the date and the metric. For example, monthly revenue around a pricing change:
Minimum 16 rows · Best with 24-500 evenly spaced periods with the intervention near the middle
Standard-library analysis: did the intervention shift the level or the slope? Map a date column and a numeric series, tell it when the intervention happened (or let it assume the series midpoint, clearly flagged), and get a segmented regression: the immediate jump in the series at the break (level change) and the bend in the trend (slope change), each with a 95% confidence interval and p-value, plus the pre-intervention trend projected forward as a counterfactual and the gap between actual and counterfactual at the end of the series, in units and percent. A lag-1 residual autocorrelation check warns when the classical p-values are anti-conservative.
The actual series, the segmented fit, and the old trend projected forward — the gap that opens at the break is the effect.
The two headline numbers: the immediate jump (level change) and the trend break (slope change), each with a confidence interval and p-value.
How far the series has diverged from where its old trend was heading, in units and percent.
How the break was placed, the model, the autocorrelation check, and the assumptions the causal read rests on.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Did the intervention shift the level or the slope?
Map your date and value columns and give the intervention date. You get the immediate jump and the trend break at that date, each with a confidence interval and p-value, plus the gap between where you are and where the old trend was headed.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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