InversePropensityWeighting#

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

A class to analyse inverse propensity weighting experiments.

Parameters:

data (NativeDataFrame) – Any eager dataframe Narwhals supports, such as pandas, Polars, or PyArrow. Converted to pandas internally.
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 (PropensityScore | None) – A PyMC model. Defaults to PropensityScore.

Notes

Lazy lifecycle

Construction validates the inputs and builds the design matrices — no sampling happens. Call fit() to build the propensity graph and draw prior and posterior samples. The IPW diagnostics (get_ate(), plot_ate(), plot_balance_ecdf()) are posterior-only and require fit() first.

Estimate extraction

Fitting produces posterior propensity-score draws. get_ate() post-processes one draw at a time: "raw" and "robust" contrast inverse-probability-weighted mean outcomes for the treated and control potential outcomes, "overlap" contrasts overlap-weighted means for the overlap population, and "doubly_robust" augments inverse-probability weighting with separate OLS outcome regressions before averaging over all observations.

Examples

>>> 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,
...         },
...     ),
... ).fit()

Methods

`InversePropensityWeighting.build`()	Construct the model graph without sampling anything.
`InversePropensityWeighting.effect_summary`(*)	Raise because unified effect summaries are unavailable.
`InversePropensityWeighting.fit`(**kwargs)	Run the posterior phase and populate `result`.
`InversePropensityWeighting.generate_report`(*)	Generate a self-contained HTML report for this experiment.
`InversePropensityWeighting.get_ate`(i, idata)	Compute the Average Treatment Effect for a single posterior sample.
`InversePropensityWeighting.input_validation`()	Validate the input data and model formula for correctness.
`InversePropensityWeighting.make_doubly_robust_adjustment`(ps)	Compute doubly-robust adjusted outcomes.
`InversePropensityWeighting.make_overlap_adjustments`(ps)	Compute inverse-propensity-weighted outcomes using the overlap scheme.
`InversePropensityWeighting.make_raw_adjustments`(ps)	Compute inverse-propensity-weighted outcomes using the raw (basic) scheme.
`InversePropensityWeighting.make_robust_adjustments`(ps)	Compute inverse-propensity-weighted outcomes using the robust (Horvitz-Thompson) scheme.
`InversePropensityWeighting.plot`(*[, show, ...])	Plot the results.
`InversePropensityWeighting.plot_ate`([idata, ...])	Plot the Average Treatment Effect and propensity score distributions.
`InversePropensityWeighting.plot_balance_ecdf`(...)	Plot the empirical CDF of a covariate before and after IPW adjustment.
`InversePropensityWeighting.print_coefficients`([...])	Ask the model to print its posterior coefficients.
`InversePropensityWeighting.sample_prior_predictive`(...)	Run the optional prior phase and populate `prior_result`.
`InversePropensityWeighting.set_maketables_options`(*)	Set optional maketables rendering options for this experiment.
`InversePropensityWeighting.weighted_percentile`(...)	Compute a weighted percentile of the data.

Attributes

`has_prior_predictive`	Whether the prior phase has run (draws, and bundle where kept).
`idata`	Return fitted DataTree when the model backend supports it.
`is_built`	Whether the model graph / fit design exists (no draws implied).
`is_configured`	design matrices are ready.
`is_fitted`	Whether posterior draws and the posterior result bundle exist.
`model`	The underlying model instance.
`prior_result`	Prior-group result bundle; raises before prior sampling.
`result`	Posterior-group result bundle; raises before `fit()`.
`supports_bayes`
`supports_ols`
`supports_pymc_forecast`
`labels`
`data`

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

Parameters:

data (DataFrameLike)
formula (str)
outcome_variable (str)
weighting_scheme (str)
model (PropensityScore | None)

Return type:

None

classmethod __new__(*args, **kwargs)#