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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import pandas as pd\n", | |
"from causality.analysis.dataframe import CausalDataFrame" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Let's confound $X$ and $Y$ with a couple of $Z$ variables, $z_1$ and $z_2$" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"N = 1000\n", | |
"lower = -1\n", | |
"upper = 1\n", | |
"z1 = np.random.uniform(lower, upper, size=N)\n", | |
"z2 = np.random.uniform(lower, upper, size=N)\n", | |
"x = np.random.uniform(lower, upper, size=N) + (z1 + z2)/2.\n", | |
"z = z1 + z2\n", | |
"y = np.random.normal(size=N) - x + 2.* z\n", | |
"X = CausalDataFrame({'x': x, 'y': y, 'z1': z1, 'z2': z2})" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The causal effect of $X$ on $Y$ is given by cutting the edge from $Z_1$ and $Z_2$ into $X$. Taking the expectation of the $Y$ formula, using that $Z$ and $X$ are now independent, we see the conditional expecation is now $y(x) = E[Y|X=x] = E[U - X + 2Z|X=x] = 0 - E[X|X=x] + 2 E[Z|X=x] = - x + 2 E[Z] = - x$, where $U \\sim \\mathcal{N}(0,1)$. The naive plot looks nothing like $y = -x$:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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V+Wq92vf908LgOersDDKjPq0GuWF4miysz5rR9XkyxsYewkuKgyywkyYuG7Tom7bfF/J5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gwneXfeb5L22OQQP3NqjFTC4tzMKKm4HXThfHCiCttO75Vw1/zhU4d46dRY3WvFLItk3CZfrlB2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\nrkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fc2d01575d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"X.plot(x='x', y='y', style='bo', alpha=0.2, kind='scatter');" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"but let's check the plot after back-door adjustment for $Z_1$ and $Z_2$ ..." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc287c741d0>" | |
] | |
}, | |
"execution_count": 11, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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+thGv/O46sg8dp+vIOTw/Vc3lLocCQERCUoe6VzBzUAq3NqrEy99spNOwWSzY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\nASAiEqb+P0R653N2xk2zAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fc287c24c90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"X.zplot(x='x', y='y', z={'z1': 'c', 'z2': 'c'}, kind='line', model_type='kernel')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"looks great!" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.12" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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