causaleffect
causaleffect
is a Python library for computing conditional and non-conditional causal effects.
Installation
Use the package manager pip to install causaleffect
.
pip install causaleffect
If one wants to plot graphs with the plotGraph
function, either the pycairo
library (version 1.17.2
or later) or the cairocffi
library is also required.
Usage
If we want to compute the causal effect P(y|do(X=x)) from the causal diagram shown below,
we first create and display the graph:
import causaleffect
G = causaleffect.createGraph(['X<->Y', 'Z->Y', 'X->Z', 'W->X', 'W->Z'])
causaleffect.plotGraph(G)
which renders the following image
Then we can compute the causal effect by executing:
P = causaleffect.ID({'Y'}, {'X'}, G)
P.printLatex()
The code above computes the causal effect, and returns a string encoding the distribution in LaTeX notation:
'\sum_{w, z}P(w)P(z|w, x)\left(\sum_{x}P(x|w)P(y|w, x, z)\right)'
This string, in LaTeX, is
Examples
Some examples from the dissertation can be found in this repository:
Figure number | Example file |
---|---|
Figure 3.5 (a) | example_1.py |
Figure 3.6 (a) | example_2.py |
Figure 3.6 (b) | example_3.py |
Figure 3.10 | example_4.py |
Figure 3.12 | example_5.py |
Figure 3.13 | example_6.py |
Figure 3.15 (a) | example_7.py |
Figure 3.15 (b) | example_8.py |
Figure 3.16 | example_9.py |