Structural Causal Models
Project description
OS | Status |
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Linux | |
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A Python package implementing Structural Causal Models (SCM).
The library uses the CAS library SymPy to allow the user to state arbitrary assignment functions and noise distributions as supported by SymPy and builds the DAG with networkx.
It supports the features:
- Sampling
- Intervening
- Plotting
- Printing
and by extension all methods on a DAG provided by networkx after accessing the member variable dag
Installation
Either install via pip
pip install scmodels
or via cloning the repository and running the setup.py file
git clone https://github.com/maichmueller/scmodels
cd scmodels
python setup.py install
Example usage
To build the DAG
with the assignments
one can describe the assignments as strings
from scmodels import SCM
myscm = SCM(
[
"Z = N, N ~ LogLogistic(alpha=1, beta=1)",
"X = N * 3 * Z ** 2, N ~ LogNormal(mean=1, std=1)",
"Y = N + 2 * Z + sqrt(X), N ~ Normal(mean=2, std=1)"
]
)
or build the assignments piecewise themselves via an assignment map
from sympy.stats import LogLogistic, LogNormal, Normal
assignment_map = {
"Z": (
"N",
LogLogistic("N", alpha=1, beta=1)
),
"X": (
"N * 3 * Z ** 2",
LogNormal("N", mean=1, std=1),
),
"Y": (
"N + 2 * Z + sqrt(X)",
Normal("N", mean=2, std=1),
),
}
myscm = SCM(assignment_map)
The SCM supports a form of pretty printing its current setup, which includes mentioning active interventions and the assignments
print(myscm)
Structural Causal Model of 3 variables: Z, X, Y
Following variables are actively intervened on: []
Current Assignments are:
Z := f(N) = N [ N ~ LogLogistic(alpha=1, beta=1) ]
X := f(N, Z) = N * 3 * Z ** 2 [ N ~ LogNormal(mean=1, std=1) ]
Y := f(N, X, Z) = N + 2 * Z + sqrt(X) [ N ~ Normal(mean=2, std=1) ]
One can easily perform interventions on the variables, e.g. a Do-intervention
myscm.do_intervention([("X", 1)])
and sample as many samples from it as desired
myscm.sample(5)
X | Z | Y | |
---|---|---|---|
0 | 1 | 1.037087 | 6.206237 |
1 | 1 | 0.207293 | 2.278741 |
2 | 1 | 4.479058 | 11.089535 |
3 | 1 | 0.574231 | 4.807162 |
4 | 1 | 7.216779 | 17.946997 |
The current intervention is also noted in its string representation
print(myscm)
Structural Causal Model of 3 variables: Z, X, Y
Following variables are actively intervened on: ['X']
Current Assignments are:
Z := f(N) = N [ N ~ LogLogistic(alpha=1, beta=1) ]
X := f(N) = 1 [ N ~ LogNormal(mean=1, std=1) ]
Y := f(N, X, Z) = N + 2 * Z + sqrt(X) [ N ~ Normal(mean=2, std=1) ]
which can be restored to the initial status via undoing the intervention
myscm.undo_intervention()
If you have graphviz installed, you can also use it to plot the DAG easily
myscm.plot(node_size=1000, alpha=1)
No history capture as of yet.
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