pymc.model.transform.conditioning.do

pymc.model.transform.conditioning.do(model, vars_to_interventions, prune_vars=False)

Replace model variables by intervention variables.

Intervention variables will either show up as Data or Deterministics in the new model, depending on whether they depend on other RandomVariables or not.

Parameters:

model: PyMC Model

vars_to_interventions: Dict of variable or name to TensorLike

Dictionary that maps model variables (or names) to intervention expressions. Intervention expressions must have a shape and data type that is compatible with the original model variable.

prune_vars: bool, defaults to False

Whether to prune model variables that are not connected to any observed variables, after the interventions.

Returns:

new_model: PyMC model

A distinct PyMC model with the relevant variables replaced by the intervention expressions. All remaining variables are cloned and can be retrieved via new_model[“var_name”].

Examples

import pymc as pm

with pm.Model() as m:
    x = pm.Normal("x", 0, 1)
    y = pm.Normal("y", x, 1)
    z = pm.Normal("z", y + x, 1)

# Dummy posterior, same as calling `pm.sample`
idata_m = az.from_dict({rv.name: [pm.draw(rv, draws=500)] for rv in [x, y, z]})

# Replace `y` by a constant `100.0`
with pm.do(m, {y: 100.0}) as m_do:
    idata_do = pm.sample_posterior_predictive(idata_m, var_names="z")