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Results tagged with causalimpact
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user 130405
CausalImpact is an R package for estimating the effect of an intervention on a time series. Use this tag for any on-topic question that (a) involves CausalImpact package either as a critical part of the question or expected answer, & (b) is not just about how to use CausalImpact.
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CausalImpact plot upper and lower bounds
For point effects, the shaded blue area shows pointwise 95% credible intervals, while the summary table reports the 95% credible interval for the average effect (mean over the whole post-intervention …
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How to test the stability assumption when using the CausalImpact package in R?
Because CausalImpact uses a model fitted in the pre-period to predict the response in the post-period under the assumption that no intervention happened; the effect is calculated as the difference between …
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Causal impact R - Is it possible to model multiple the pre and post periods? Indivudally for...
No, the CausalImpact R package currently does not support that. …
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Using CausalImpact in R, observed data affected by event that could not affect priors
It is possible to have a gap between pre- and post-period by specifying the arguments such pre.period[1] does not immediately follow pre.period[2]. With this, it is possible to exclude some time perio …
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Incorporating Fixed Effects with Causal Impact
This is currently not possible with the CausalImpact R package. …
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How is the posterior tail-area probability calculated?
The CausalImpact paper describes the calculation of the tail-area probability in detail (see section 2.3). …
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How to use a custom model in CausalImpact where preperiod.start is not right after preperiod...
It wouldn't be too hard to implement this feature, but it would require a change in the CausalImpact() interface. …
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Is it possible to predict the pre-intervention period rather than the post-period using Goog...
This is technically possible by reverting the time series, and makes sense given the short actual pre-period (which will most likely not give sufficient power to fit a reasonable model).
The interpre …
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How to study the causal impact on multiple time series of interest against multiple baselines
Given the high variability of the page hit data, there is probably not much you can do to fit models for individual players. However, you could at least try to fit a model for the sum of the daily pag …
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CausalImpact with Custom BSTS Model
But even including variables that are not predictive should not hurt the model as CausalImpact performs automatic variable selection (a random noise variable should automatically be excluded). …
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BSTS employing purposedly spurious regressors for causal impact analysis
To make sure also 1 is not the case, you could fit an additional CausalImpact model using one of the control time series as response, and the remaining ones as covariates (and repeating this for each control … time series): if the control time series are indeed not affected by the intervention, CausalImpact should fail to find a significant impact here. …
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How can you quantify the effect of a campaign in CausalImpact?
This is exactly what is reported as 'Absolute effect' in the summary of a CausalImpact object, say, impact:
summary(impact)
(See the examples in the package documentation, example("CausalImpact").) …
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Reduce credible intervals in Causal Impact model
The line plot shows that the scale of daily sales differ by almost an order of magnitude; so log-transforming daily sales and working on a log scale could help to get better models (it's easier for CausalImpact … Overall, the fact that credible bands are widening quickly shows that CausalImpact currently fits a strong random walk component, which means the variability in the response is currently not well explained …
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Accepted
What does Posterior tail-area probability mean in Causal Impact?
Technically, this value is calculated as follows:
Based on parameter priors and the data you provide, CausalImpact calculates the posterior distribution of the response variable that would be expected … In the example (to be run with example("CausalImpact") in R), the summary shows a positive effect and reports a tail-area probability of 0.001. …
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Accepted
Odd behaviour in CausalImpact (R)
One can force CausalImpact to attribute the fluctuations to noise rather than signal by adjusting the prior for the random walk component:
impact <- CausalImpact(data, pre.period, post.period, … This shows that CausalImpact does not wrongly learn from the independent time series x2: the factor accounting for the tight fits is the random walk component, not the regressor. …
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