This is the Windows app named CausalImpact whose latest release can be downloaded as CausalImpactsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
CausalImpact
DESCRIPTION:
The CausalImpact repository houses an R package that implements causal inference in time series using Bayesian structural time series models. Its goal is to estimate the effect of an intervention (e.g. a marketing campaign, policy change) on a time series outcome by predicting what would have happened in a counterfactual “no intervention” world. The package requires as input a response time series plus one or more control (covariate) time series that are assumed unaffected by the intervention, and it divides the time horizon into “pre-intervention” and “post-intervention” periods. It uses Bayesian modeling to fit a structural time series to the pre-period and extrapolate a counterfactual prediction for the post period, then compares observed vs predicted to infer the causal effect. The package supports plotting, summary tables, and verbal narratives for interpretive reports.
Features
- Bayesian structural time series model to infer counterfactuals
- Analysis of intervention effects on time series (pre/post comparison)
- Support for multiple covariate (control) time series
- Automated plotting, summary tables, and narrative output
- Diagnostics and customization of priors and model options
- Strong documentation and example workflows for real use
Programming Language
R
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/causalimpact.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.