IVEstimator

class pgmpy.estimators.IVEstimator(model)

Bases: object

Initialize IVEstimator object.

Parameters:

model: pgmpy.models.SEM

The model for which estimation need to be done.

Examples

>>> from pgmpy.models import SEM
>>> from pgmpy.estimators import IVEstimator
>>> model = SEM.from_graph(
...     ebunch=[
...         ("Z1", "X", 1.0),
...         ("Z2", "X", 1.0),
...         ("Z2", "W", 1.0),
...         ("W", "U", 1.0),
...         ("U", "X", 1.0),
...         ("U", "Y", 1.0),
...         ("X", "Y", 1.0),
...     ],
...     latents=["U"],
...     err_var={"Z1": 1, "Z2": 1, "W": 1, "X": 1, "U": 1, "Y": 1},
... )
>>> estimator = IVEstimator(model)

fit(X, Y, data, ivs=None, civs=None)

Estimates the parameter X -> Y.

Parameters:

X: str

The covariate variable of the parameter being estimated.

Y: str

The predictor variable of the parameter being estimated.

data: pd.DataFrame

The data from which to learn the parameter.

ivs: List (default: None)

List of variable names which should be used as Instrumental Variables (IV). If not specified, tries to find the IVs from the model structure, fails if can’t find either IV or Conditional IV.

civs: List of tuples (tuple form: (var, coditional_var))

List of conditional IVs to use for estimation. If not specified, tries to find the IVs from the model structure, fails if can’t find either IV or Conditional IVs.

Returns:

tuple: (float, statsmodels.regression.linear_model.RegressionResultsWrapper)

A tuple where the first element is the estimated causal parameter for X -> Y, and the second element is the fitted OLS results object from the second stage regression (a RegressionResultsWrapper). Call .summary() on this object to get the textual summary.

Examples

>>> from pgmpy.models import SEM
>>> from pgmpy.estimators import IVEstimator
>>> model = SEM.from_graph(
...     ebunch=[
...         ("Z1", "X", 1.0),
...         ("Z2", "X", 1.0),
...         ("Z2", "W", 1.0),
...         ("W", "U", 1.0),
...         ("U", "X", 1.0),
...         ("U", "Y", 1.0),
...         ("X", "Y", 1.0),
...     ],
...     latents=["U"],
...     err_var={"Z1": 1, "Z2": 1, "W": 1, "X": 1, "U": 1, "Y": 1},
... )
>>> data = model.to_lisrel().generate_samples(500)
>>> estimator = IVEstimator(model)
>>> param, results = estimator.fit(X="X", Y="Y", data=data)