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) . 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.

Installation

install.packages("CausalImpact")
library(CausalImpact)

Getting started

Video tutorial

Documentation and examples

Further resources

News

Reference manual

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install.packages("CausalImpact")

1.2.3 by Alain Hauser, 9 months ago


https://google.github.io/CausalImpact/


Browse source code at https://github.com/cran/CausalImpact


Authors: Kay H. Brodersen <[email protected]>, Alain Hauser <[email protected]>


Documentation:   PDF Manual  


Apache License 2.0 | file LICENSE license


Imports assertthat, Boom, dplyr, ggplot2, zoo

Depends on bsts

Suggests knitr, testthat


See at CRAN