When is A/B testing not feasible, and what is an alternative?
Tests your grasp of causal inference when randomization isn't possible. A great answer names a scenario (like a regional launch), proposes Difference-in-Differences (DiD), and explains its core 'parallel trends' assumption.
This tests your grasp of causal inference beyond A/B tests. A strong answer first describes a scenario where randomization is impossible, like a geographic rollout or marketplace policy change. Then, it introduces Difference-in-Differences (DiD), explaining that it measures impact by comparing the change in the treatment group to the change in the control group. Finally, it clearly states the core 'parallel trends' assumption: that both groups would have trended similarly without the intervention.
Read the original → statsig.com
- #causal inference
- #analytics
- #metrics
- #a/b testing
- #statistics
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