InstrumentalVariableRegression

class causalpy.pymc_models.InstrumentalVariableRegression

Custom PyMC model for instrumental linear regression

Methods

InstrumentalVariableRegression.__init__([...])	type sample_kwargs: Optional[Dict[str, Any]]
InstrumentalVariableRegression.add_coord(name)	Registers a dimension coordinate with the model.
InstrumentalVariableRegression.add_coords(...)	Vectorized version of Model.add_coord.
InstrumentalVariableRegression.add_named_variable(var)	Add a random graph variable to the named variables of the model.
InstrumentalVariableRegression.build_model(X, ...)	Specify model with treatment regression and focal regression data and priors
InstrumentalVariableRegression.check_start_vals(start)	Check that the starting values for MCMC do not cause the relevant log probability to evaluate to something invalid (e.g. Inf or NaN).
InstrumentalVariableRegression.compile_d2logp([...])	Compiled log probability density hessian function.
InstrumentalVariableRegression.compile_dlogp([...])	Compiled log probability density gradient function.
InstrumentalVariableRegression.compile_fn(outs, *)	Compiles an PyTensor function
InstrumentalVariableRegression.compile_logp([...])	Compiled log probability density function.
InstrumentalVariableRegression.create_value_var(...)	Create a TensorVariable that will be used as the random variable's "value" in log-likelihood graphs.
InstrumentalVariableRegression.d2logp([...])	Hessian of the models log-probability w.r.t.
InstrumentalVariableRegression.debug([...])	Debug model function at point.
InstrumentalVariableRegression.dlogp([vars, ...])	Gradient of the models log-probability w.r.t.
InstrumentalVariableRegression.eval_rv_shapes()	Evaluates shapes of untransformed AND transformed free variables.
InstrumentalVariableRegression.fit(X, Z, y, ...)	Draw samples from posterior distribution and potentially from the prior and posterior predictive distributions.
InstrumentalVariableRegression.initial_point([...])	Computes the initial point of the model.
InstrumentalVariableRegression.logp([vars, ...])	Elemwise log-probability of the model.
InstrumentalVariableRegression.logp_dlogp_function([...])	Compile an PyTensor function that computes logp and gradient.
InstrumentalVariableRegression.make_obs_var(...)	Create a TensorVariable for an observed random variable.
InstrumentalVariableRegression.name_for(name)	Checks if name has prefix and adds if needed
InstrumentalVariableRegression.name_of(name)	Checks if name has prefix and deletes if needed
InstrumentalVariableRegression.point_logps([...])	Computes the log probability of point for all random variables in the model.
InstrumentalVariableRegression.predict(X)	Predict data given input data X
InstrumentalVariableRegression.profile(outs, *)	Compiles and profiles an PyTensor function which returns outs and takes values of model vars as a dict as an argument.
InstrumentalVariableRegression.register_data_var(data)	Register a data variable with the model.
InstrumentalVariableRegression.register_rv(...)	Register an (un)observed random variable with the model.
InstrumentalVariableRegression.replace_rvs_by_values(...)	Clone and replace random variables in graphs with their value variables.
InstrumentalVariableRegression.sample_predictive_distribution([...])	Function to sample the Multivariate Normal posterior predictive Likelihood term in the IV class.
InstrumentalVariableRegression.score(X, y)	Score the Bayesian \(R^2\) given inputs X and outputs y.
InstrumentalVariableRegression.set_data(...)	Changes the values of a data variable in the model.
InstrumentalVariableRegression.set_dim(name, ...)	Update a mutable dimension.
InstrumentalVariableRegression.set_initval(...)	Sets an initial value (strategy) for a random variable.
InstrumentalVariableRegression.shape_from_dims(dims)
InstrumentalVariableRegression.to_graphviz(*)	Produce a graphviz Digraph from a PyMC model.
InstrumentalVariableRegression.update_start_vals(a, b)	Update point a with b, without overwriting existing keys.

Attributes

basic_RVs	List of random variables the model is defined in terms of (which excludes deterministics).
continuous_value_vars	All the continuous value variables in the model
coords	Coordinate values for model dimensions.
datalogp	PyTensor scalar of log-probability of the observed variables and potential terms
dim_lengths	The symbolic lengths of dimensions in the model.
discrete_value_vars	All the discrete value variables in the model
isroot
observedlogp	PyTensor scalar of log-probability of the observed variables
parent
potentiallogp	PyTensor scalar of log-probability of the Potential terms
prefix
root
unobserved_RVs	List of all random variables, including deterministic ones.
unobserved_value_vars	List of all random variables (including untransformed projections), as well as deterministics used as inputs and outputs of the model's log-likelihood graph
value_vars	List of unobserved random variables used as inputs to the model's log-likelihood (which excludes deterministics).
varlogp	PyTensor scalar of log-probability of the unobserved random variables (excluding deterministic).
varlogp_nojac	PyTensor scalar of log-probability of the unobserved random variables (excluding deterministic) without jacobian term.

__init__(sample_kwargs=None)

Parameters:

sample_kwargs (Optional[Dict[str, Any]]) – A dictionary of kwargs that get unpacked and passed to the pymc.sample() function. Defaults to an empty dictionary.

static __new__(cls, *args, model=UNSET, **kwargs)

Parameters:

model (Literal[UNSET] | None | ~pymc.model.core.Model)