{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example Interrupted Time Series (ITS) with sci-kit learn models\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"import causalpy as cp"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" month \n",
" year \n",
" t \n",
" y \n",
" \n",
" \n",
" date \n",
" \n",
" \n",
" \n",
" \n",
" 2010-01-31 \n",
" 1 \n",
" 2010 \n",
" 0 \n",
" 25.058186 \n",
" \n",
" \n",
" 2010-02-28 \n",
" 2 \n",
" 2010 \n",
" 1 \n",
" 27.189812 \n",
" \n",
" \n",
" 2010-03-31 \n",
" 3 \n",
" 2010 \n",
" 2 \n",
" 26.487551 \n",
" \n",
" \n",
" 2010-04-30 \n",
" 4 \n",
" 2010 \n",
" 3 \n",
" 31.241716 \n",
" \n",
" \n",
" 2010-05-31 \n",
" 5 \n",
" 2010 \n",
" 4 \n",
" 40.753973 \n",
" \n",
" \n",
"
\n",
"
"
]
},
"metadata": {
"image/png": {
"height": 811,
"width": 711
}
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = result.plot()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================Pre-Post Fit==================================\n",
"Formula: y ~ 1 + t + C(month)\n",
"Model coefficients:\n",
" Intercept \t 0\n",
" C(month)[T.2] \t 2.85\n",
" C(month)[T.3] \t 1.16\n",
" C(month)[T.4] \t 7.15\n",
" C(month)[T.5] \t 15\n",
" C(month)[T.6] \t 24.8\n",
" C(month)[T.7] \t 18.2\n",
" C(month)[T.8] \t 33.5\n",
" C(month)[T.9] \t 16.2\n",
" C(month)[T.10]\t 9.19\n",
" C(month)[T.11]\t 6.28\n",
" C(month)[T.12]\t 0.578\n",
" t \t 0.21\n"
]
}
],
"source": [
"result.summary(round_to=3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "CausalPy",
"language": "python",
"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
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"orig_nbformat": 4,
"vscode": {
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