The CausalImpact R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.
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References in zbMATH (referenced in 1 article )
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- Brodersen, Kay H.; Gallusser, Fabian; Koehler, Jim; Remy, Nicolas; Scott, Steven L.: Inferring causal impact using Bayesian structural time-series models (2015)