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.
install the latest release via pip
pip install causalimpact
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.
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Python Package for causal inference using Bayesian structural time-series models
We found that causalimpact demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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