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 |

Documentation

The extended documentation of this library can be found under the folder documentation of this same repository or in https://arxiv.org/abs/2107.04632.