Work In Progress
Causal inference using Bayesian structural time-series models. This package aims at defining a python equivalent of the R CausalImpact package by Google. Please refer to the package itself, its documentation or the related publication (Brodersen et al., Annals of Applied Statistics, 2015) for more information.
Preferably use a virtual environment:
git clone git@github.com:tcassou/causal_impact
cd causal_impact
virtualenv .venv
. .venv/bin/activate
pip install -r requirements.txt
Suppose we have a DataFrame data recording daily measures for three different markets y, x1 and x2, for t = 0..365).
The y time series in data is the one we will be modeling, while other columns (x1 and x2 here) will be used as a set of control time series.
>>> data
y x1 x2
0 1735.01 1014.44 1005.87
1 1709.54 1012.63 1008.18
2 1772.95 1039.04 1024.21
... ... ... ...
At t = date_inter = 280, a marketing campaing (the intervention) is run for market y. We want to understand the impact of that campaign on our measure.
from causal_impact import CausalImpact
ci = CausalImpact(data, date_inter)
ci.run()
ci.plot()
After fitting the model, and estimating what the y time series would have been without any intervention, this will typically produce the following plots:
As mentioned above, this package is still being developed. Feel free to contribute through github by sending pull requests or reporting issues.