Isolating Impact with Difference-in-Differences (DiD)
Difference-in-Differences (DiD) isolates an intervention's true effect by comparing a treatment group's change over time to a control group's. This reveals if a new feature truly boosted engagement, not just rode a general upward trend.
Difference-in-Differences (DiD) is a statistical method that isolates the true impact of an intervention by comparing a treated group to a control group, after accounting for underlying trends that affect both. It calculates the "difference in the differences" of outcomes before and after the change. This is crucial for analyzing natural experiments, like seeing if a new marketing campaign in one city actually increased sales compared to a similar city without the campaign.
Read the original → Wikipedia: Difference in differences
- #analytics
- #statistics
- #causal inference
- #quasi-experiment
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