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 <> 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.



Getting started

Video tutorial

Documentation and examples

Further resources


Reference manual

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1.2.3 by Alain Hauser, 5 months ago

Browse source code at

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