PrePostNEGD

class causalpy.experiments.prepostnegd.PrePostNEGD

A class to analyse data from pretest/posttest designs

Parameters:

• data (DataFrame) – A pandas dataframe

• 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 – A PyMC model

Example

>>> 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,
...         }
...     )
... )
>>> 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]
    sigma          0.5, 94% HDI [0.5, 0.6]

Methods

PrePostNEGD.__init__(data, formula, ...[, model])
PrePostNEGD.bayesian_plot([round_to])	Generate plot for ANOVA-like experiments with non-equivalent group designs.
PrePostNEGD.input_validation()	Validate the input data and model formula for correctness
PrePostNEGD.ols_plot(*args, **kwargs)	Abstract method for plotting the model.
PrePostNEGD.plot(*args, **kwargs)	Plot the model.
PrePostNEGD.print_coefficients([round_to])	Ask the model to print its coefficients.
PrePostNEGD.summary([round_to])	Print summary of main results and model coefficients.

Attributes

idata	Return the InferenceData object of the model.
supports_bayes
supports_ols

__init__(data, formula, group_variable_name, pretreatment_variable_name, model=None, **kwargs)

Parameters:

• data (DataFrame)

• formula (str)

• group_variable_name (str)

• pretreatment_variable_name (str)

__new__(**kwargs)