InstrumentalVariable#

class causalpy.pymc_experiments.InstrumentalVariable[source]#

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, }

Methods

`InstrumentalVariable.__init__`(...[, model, ...])
`InstrumentalVariable.get_2SLS_fit`()	Two Stage Least Squares Fit
`InstrumentalVariable.get_naive_OLS_fit`()	Naive Ordinary Least Squares
`InstrumentalVariable.print_coefficients`([...])	Prints the model coefficients

Attributes

`expt_type`
`idata`	Access to the models InferenceData object
`model`

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

Parameters: