PrePostNEGD#

class causalpy.experiments.prepostnegd.PrePostNEGD[source]#

A class to analyse data from pretest/posttest designs.

Parameters:

data (NativeDataFrame) – Any eager dataframe Narwhals supports, such as pandas, Polars, or PyArrow. Converted to pandas internally.
formula (str) – A statistical model formula.
group_variable_name (str) – Name of the column in data for the group variable; should be either binary or boolean.
pretreatment_variable_name (str) – Name of the column in data for the pretreatment variable.
model (PyMCModel | None) – A PyMC model. Defaults to LinearRegression.

Notes

Estimate extraction

The reported causal_impact is the posterior coefficient on the treatment-group term, conditional on the pretreatment outcome and any other formula covariates. Treated and untreated prediction curves are also computed for visualization, but they do not determine the reported scalar effect. With the current additive identity-link model, the treatment coefficient equals the corresponding conditional prediction contrast.

Lazy lifecycle: construction only validates inputs and builds design matrices — nothing is sampled. Call fit() to draw posterior samples (and sample_prior_predictive() for prior predictive checks); read methods such as plot(), summary(), and effect_summary() require the corresponding draw group.

Examples

>>> import causalpy as cp
>>> df = cp.load_data("anova1")
>>> seed = 42
>>> result = cp.PrePostNEGD(
...     df,
...     formula="post ~ 1 + C(group) + pre",
...     group_variable_name="group",
...     pretreatment_variable_name="pre",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     ),
... ).fit()
>>> result.summary(round_to=1)
==================Pretest/posttest Nonequivalent Group Design===================
Formula: post ~ 1 + C(group) + pre

Results:
Causal impact = 2, $CI_{94%}$[2, 2]
Model coefficients:
    Intercept      -0.5, 94% HDI [-1, 0.2]
    C(group)[T.1]  2, 94% HDI [2, 2]
    pre            1, 94% HDI [1, 1]
    y_hat_sigma    0.5, 94% HDI [0.5, 0.6]

Methods

`PrePostNEGD.build`()	Construct the model graph without sampling anything.
`PrePostNEGD.effect_summary`(*[, group, ...])	Generate a decision-ready summary of causal effects for PrePostNEGD.
`PrePostNEGD.fit`(**kwargs)	Run the posterior phase and populate `result`.
`PrePostNEGD.generate_report`(*[, ...])	Generate a self-contained HTML report for this experiment.
`PrePostNEGD.input_validation`()	Validate the input data and model formula for correctness.
`PrePostNEGD.plot`(*[, group, round_to, ...])	Plot the pre-post non-equivalent group design results.
`PrePostNEGD.print_coefficients`([round_to])	Ask the model to print its posterior coefficients.
`PrePostNEGD.sample_prior_predictive`(**kwargs)	Run the optional prior phase and populate `prior_result`.
`PrePostNEGD.set_maketables_options`(*[, hdi_prob])	Set optional maketables rendering options for this experiment.
`PrePostNEGD.summary`([round_to])	Print summary of main results and model coefficients.

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, group_variable_name, pretreatment_variable_name, model=None)[source]#

Parameters:

data (DataFrameLike)
formula (str)
group_variable_name (str)
pretreatment_variable_name (str)
model (PyMCModel | None)

Return type:

None

classmethod __new__(*args, **kwargs)#