Causal Impact Campaign Skill
Measures causal impact of marketing campaigns using Bayesian structural time series analysis.
Installation
git submodule add https://github.com/wan-huiyan/causal-impact-campaign.git .claude/skills/causal-impact-campaign About this Tool
Skill that measures the causal impact of marketing campaigns using Bayesian structural time series models. Determines whether a campaign actually caused observed changes in metrics, going beyond correlation to establish true attribution. Essential for data-driven marketing teams evaluating campaign ROI.
Use Case Examples
- Measure true causal impact of a marketing campaign on revenue
- Evaluate whether ad spend increases actually drove conversions
- Run Bayesian analysis to separate campaign effect from seasonal trends
MarketCore Editorial Review
MarketCore Editorial Review
One of the few marketing tools in the directory that does real statistical work. Using Bayesian structural time series to measure campaign causality is genuinely sophisticated — this is not prompt-wrapped guesswork. Essential for data-driven teams who need defensible attribution rather than last-click correlation.
Best For
- Determine whether a campaign actually caused revenue lift or benefited from external factors
- Evaluate paid media spend effectiveness against counterfactual scenarios
- Produce statistically rigorous campaign reports for finance and executive stakeholders
Code Quality
The underlying methodology (Google's CausalImpact library) is academically sound. Implementation is functional but sparse — requires R or Python to be installed. Not beginner-friendly but technically correct.
Limitations
Advanced prerequisite knowledge required — users need to understand statistical time series concepts. Requires clean historical data with a well-defined pre/post campaign period. Documentation is minimal.
Reviewed 2026-04-01
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Score Breakdown
Overall Score
Based on 5 dimensions
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