jamalsenouci / Causalimpact
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CausalImpact
Disclaimer: CURRENTLY WIP
TO DO
- Estimation is MLE not Bayesian
- More unit tests
A Python package for causal inference using Bayesian structural time-series models
This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact.
This 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
get the latest version from https://github.com/jamalsenouci/causalimpact:
pip install git+ssh://github.com/jamalsenouci/causalimpact.git
Getting started
Further resources
- Manuscript: Brodersen et al., Annals of Applied Statistics (2015)
- Discussion forum: http://stats.stackexchange.com/
Bugs
The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request.