causalimpact

We have hosted the application causalimpact in order to run this application in our online workstations with Wine or directly.

Quick description about causalimpact:

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: