InstrumentalVariable

class causalpy.experiments.instrumental_variable.InstrumentalVariable

A class to analyse instrumental variable style experiments.

Parameters:

• instruments_data (DataFrame) – A pandas dataframe of instruments for our treatment variable. Should contain instruments Z, and treatment t

• data (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 – A PyMC model

• priors –

  An optional dictionary of priors for the mus and sigmas of both regressions. If priors are not specified we will substitue MLE estimates for the beta coefficients. Greater control can be achieved by specifying the priors directly e.g. 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.bayesian_plot(*args, ...)	Abstract method for plotting the model.
InstrumentalVariable.get_2SLS_fit()	Two Stage Least Squares Fit
InstrumentalVariable.get_naive_OLS_fit()	Naive Ordinary Least Squares
InstrumentalVariable.input_validation()	Validate the input data and model formula for correctness
InstrumentalVariable.ols_plot(*args, **kwargs)	Abstract method for plotting the model.
InstrumentalVariable.plot([round_to])	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

__init__(instruments_data, data, instruments_formula, formula, model=None, priors=None, **kwargs)

Parameters:

• instruments_data (DataFrame)

• data (DataFrame)

• instruments_formula (str)

• formula (str)

__new__(**kwargs)