InversePropensityWeighting#

class causalpy.experiments.inverse_propensity_weighting.InversePropensityWeighting[source]#

A class to analyse inverse propensity weighting experiments.

Parameters:

data (pd.DataFrame) – A pandas dataframe.
formula (str) – A statistical model formula for the propensity model.
outcome_variable (str) – A string denoting the outcome variable in data to be reweighted.
weighting_scheme (str) – A string denoting which weighting scheme to use among: ‘raw’, ‘robust’, ‘doubly robust’ or ‘overlap’. See Aronow and Miller “Foundations of Agnostic Statistics” for discussion and computation of these weighting schemes.
model (BaseExperiment, optional) – A PyMC model. Defaults to None.

Example

>>> import causalpy as cp
>>> df = cp.load_data("nhefs")
>>> seed = 42
>>> result = cp.InversePropensityWeighting(
...     df,
...     formula="trt ~ 1 + age + race",
...     outcome_variable="outcome",
...     weighting_scheme="robust",
...     model=cp.pymc_models.PropensityScore(
...         sample_kwargs={
...             "draws": 100,
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         },
...     ),
... )

Methods

`InversePropensityWeighting.__init__`(data, ...)
`InversePropensityWeighting.fit`(args, *kwargs)
`InversePropensityWeighting.get_ate`(i, idata)
`InversePropensityWeighting.get_plot_data`(...)	Recover the data of an experiment along with the prediction and causal impact information.
`InversePropensityWeighting.get_plot_data_bayesian`(...)	Abstract method for recovering plot data.
`InversePropensityWeighting.get_plot_data_ols`(...)	Abstract method for recovering plot data.
`InversePropensityWeighting.input_validation`()	Validate the input data and model formula for correctness
`InversePropensityWeighting.make_doubly_robust_adjustment`(ps)	The doubly robust weighting scheme is also discussed in Aronow and Miller, but a bit more generally than our implementation here.
`InversePropensityWeighting.make_overlap_adjustments`(ps)	This weighting scheme was adapted from Lucy D’Agostino McGowan's blog on Propensity Score Weights referenced in the primary CausalPy explainer notebook
`InversePropensityWeighting.make_raw_adjustments`(ps)	This estimator is discussed in Aronow and Miller as the simplest of base form of inverse propensity weighting schemes
`InversePropensityWeighting.make_robust_adjustments`(ps)	This estimator is discussed in Aronow and Miller's book as being related to the Horvitz Thompson method
`InversePropensityWeighting.plot`(args, *kwargs)	Plot the model.
`InversePropensityWeighting.plot_ate`([idata, ...])
`InversePropensityWeighting.plot_balance_ecdf`(...)	Plotting function takes a single covariate and shows the differences in the ECDF between the treatment and control groups before and after weighting.
`InversePropensityWeighting.print_coefficients`([...])	Ask the model to print its coefficients.
`InversePropensityWeighting.weighted_percentile`(...)	perc : percentile in [0-1]!

Attributes

`idata`	Return the InferenceData object of the model.
`supports_bayes`
`supports_ols`
`labels`

__init__(data, formula, outcome_variable, weighting_scheme, model=None, **kwargs)[source]#

Parameters:

data (DataFrame)
formula (str)
outcome_variable (str)
weighting_scheme (str)
model (BaseExperiment | None)
kwargs (dict)

Return type:

None

classmethod __new__(*args, **kwargs)#