Example Interrupted Time Series (ITS) with sci-kit learn models

This notebook shows an example of using interrupted time series, where we do not have untreated control units of a similar nature to the treated unit and we just have a single time series of observations and the predictor variables are simply time and month.

import pandas as pd
from sklearn.linear_model import LinearRegression

import causalpy as cp
WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
%config InlineBackend.figure_format = 'retina'

Load data

df = (
    cp.load_data("its")
    .assign(date=lambda x: pd.to_datetime(x["date"]))
    .set_index("date")
)

treatment_time = pd.to_datetime("2017-01-01")
df.head()
month year t y
date
2010-01-31 1 2010 0 25.058186
2010-02-28 2 2010 1 27.189812
2010-03-31 3 2010 2 26.487551
2010-04-30 4 2010 3 31.241716
2010-05-31 5 2010 4 40.753973

Run the analysis

result = cp.skl_experiments.InterruptedTimeSeries(
    df,
    treatment_time,
    formula="y ~ 1 + t + C(month)",
    model=LinearRegression(),
)

Examine the results

fig, ax = result.plot()
../_images/211094430f4ea28e5f14658ad7cf63637e4fb5cede73a2cacbc3106fadaeb9e4.png