InversePropensityWeighting#

class causalpy.pymc_experiments.InversePropensityWeighting[source]#

A class to analyse inverse propensity weighting experiments.

Parameters:

:param outcome_variable: A string denoting the outcome variable in datq to be reweighted

Parameters:

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 – A PyMC model

Methods

`InversePropensityWeighting.__init__`(data, ...)
`InversePropensityWeighting.get_ate`(i, idata)
`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_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`([...])	Prints the model coefficients
`InversePropensityWeighting.weighted_percentile`(...)	perc : percentile in [0-1]!

Attributes

`expt_type`
`idata`	Access to the models InferenceData object
`model`

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

Parameters: