Inferring Causal Effects using Bayesian Structural Time-Series Models
Implements a Bayesian approach to causal impact estimation in time
series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
See the package documentation on GitHub
<https://google.github.io/CausalImpact/> to get started.
An R package for causal inference using Bayesian structural time-series models
This 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.
As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions.
For questions on the statistics behind CausalImpact: Cross Validated
For questions on how to use the CausalImpact R package: Stack Overflow
Functions in CausalImpact
|CausalImpact||Inferring causal impact using structural time-series models|
|as.CausalImpact||Coercion to a CausalImpact object|
|CausalImpactMethods||Printing and plotting a CausalImpact object|
Vignettes of CausalImpact
Last month downloads
|Copyright||Copyright (C) 2014-2020 Google, Inc.|
|License||Apache License 2.0 | file LICENSE|
|Packaged||2021-02-21 19:35:05 UTC; husi|
|Date/Publication||2021-02-23 12:20:46 UTC|
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