InstrumentalVariable#

class causalpy.experiments.instrumental_variable.InstrumentalVariable[source]#

A class to analyse instrumental variable style experiments.

Parameters:

instruments_data (pd.DataFrame) – A pandas dataframe of instruments for our treatment variable. Should contain instruments Z, and treatment t.
data (pd.DataFrame) – A pandas dataframe of covariates for fitting the focal regression of interest. Should contain covariates X including treatment t and outcome y.
instruments_formula (str) – A statistical model formula for the instrumental stage regression, e.g. t ~ 1 + z1 + z2 + z3.
formula (str) – A statistical model formula for the focal regression, e.g. y ~ 1 + t + x1 + x2 + x3.
model (BaseExperiment, optional) – A PyMC model. Defaults to None.
priors (dict, optional) – Dictionary of priors for the mus and sigmas of both regressions. If priors are not specified we will substitute MLE estimates for the beta coefficients. Example: priors = {"mus": [0, 0], "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}.

Example

>>> import pandas as pd
>>> import causalpy as cp
>>> from causalpy.pymc_models import InstrumentalVariableRegression
>>> import numpy as np
>>> N = 100
>>> e1 = np.random.normal(0, 3, N)
>>> e2 = np.random.normal(0, 1, N)
>>> Z = np.random.uniform(0, 1, N)
>>> ## Ensure the endogeneity of the the treatment variable
>>> X = -1 + 4 * Z + e2 + 2 * e1
>>> y = 2 + 3 * X + 3 * e1
>>> test_data = pd.DataFrame({"y": y, "X": X, "Z": Z})
>>> sample_kwargs = {
...     "tune": 1,
...     "draws": 5,
...     "chains": 1,
...     "cores": 4,
...     "target_accept": 0.95,
...     "progressbar": False,
... }
>>> instruments_formula = "X  ~ 1 + Z"
>>> formula = "y ~  1 + X"
>>> instruments_data = test_data[["X", "Z"]]
>>> data = test_data[["y", "X"]]
>>> iv = cp.InstrumentalVariable(
...     instruments_data=instruments_data,
...     data=data,
...     instruments_formula=instruments_formula,
...     formula=formula,
...     model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs),
... )

Methods

`InstrumentalVariable.__init__`(...[, model, ...])
`InstrumentalVariable.fit`(args, *kwargs)
`InstrumentalVariable.get_2SLS_fit`()	Two Stage Least Squares Fit
`InstrumentalVariable.get_naive_OLS_fit`()	Naive Ordinary Least Squares
`InstrumentalVariable.get_plot_data`(*args, ...)	Recover the data of an experiment along with the prediction and causal impact information.
`InstrumentalVariable.get_plot_data_bayesian`(...)	Abstract method for recovering plot data.
`InstrumentalVariable.get_plot_data_ols`(...)	Abstract method for recovering plot data.
`InstrumentalVariable.input_validation`()	Validate the input data and model formula for correctness
`InstrumentalVariable.plot`(args, *kwargs)	Plot the results
`InstrumentalVariable.print_coefficients`([...])	Ask the model to print its coefficients.
`InstrumentalVariable.summary`([round_to])	Print summary of main results and model coefficients.

Attributes

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

__init__(instruments_data, data, instruments_formula, formula, model=None, priors=None, **kwargs)[source]#

Parameters:

instruments_data (DataFrame)
data (DataFrame)
instruments_formula (str)
formula (str)
model (BaseExperiment | None)
priors (dict | None)
kwargs (dict)

Return type:

None

classmethod __new__(*args, **kwargs)#