TFP CausalImpact

This Python 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 [1].

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.

TFP CausalImpact is a Python +
TensorFlow Probability
implementation of the
CausalImpact R package developed at
Google by Kay Brodersen and Alain Hauser. TFP CausalImpact is based on both
the original R package and on a Python version
https://github.com/dafiti/causalimpact developed at Dafiti by Willian Fuks.
TFP CausalImpact was developed at Google by Colin Carroll, David Moore,
Jacob Burnim, Kyle Loveless, and Susanna Makela.

This is not an officially supported Google product.

[1] Inferring causal impact using Bayesian structural time-series models.
Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy,
Steven L. Scott. Annals of Applied Statistics, vol. 9 (2015), pp. 247-274.
https://research.google/pubs/pub41854/

Getting Started

TFP CausalImpact can be installed via pip:

pip install tfp-causalimpact

And imported as:

import causalimpact

See also the Quick-Start Guide.

Development

Clone TFP CausalImpact, install the development dependencies, and run the unit
tests with:

git clone https://github.com/google/tfp-causalimpact.git tfp_causalimpact
cd tfp_causalimpact

pip install flit
flit install --only-deps

pytest -vv -n auto