Paper #1 - Link
Key Points
- The causal impact of a treatment is The difference between the observed value of the response and the (unobserved) value that would have been obtained under the alternative treatment
- Data #1 - The first is the time-series behavior of the response itself, prior to the intervention.
- Data #2 - The second is the behavior of other time series that were predictive of the target series prior to the intervention
- This selection is done on the pre-treatment portion. Value for predicting the counterfactual lies in their post-treatment behavior
Summary of Steps Link
- Fitting a Bayesian structural time series model to observed data
- Predict with Intervention
- Predict without Intervention
Paper #2 - Causal Impact Analysis for App Releases in Google Play
- Causal impact analysis uses a control set: a set of unaffected data vectors
- Experiment using different control sets in the study
- The agreement is defined as (YY+NN)/total
- YY indicates a significant change as detected on both datasets
- NN indicates no significant change detected on both datasets
Keep Thinking!!!
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