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();