PropensityScore#

class causalpy.pymc_models.PropensityScore[source]#

Custom PyMC model for inverse propensity score models

Defines the PyMC model

\[ \begin{align}\begin{aligned}\beta &\sim \mathrm{Normal}(0, 1)\\\sigma &\sim \mathrm{HalfNormal}(1)\\\mu &= X * \beta\\p &= logit^{-1}(mu)\\t &\sim \mathrm{Bernoulli}(p)\end{aligned}\end{align} \]

Example

>>> import causalpy as cp
>>> import numpy as np
>>> from causalpy.pymc_models import PropensityScore
>>> df = cp.load_data('nhefs')
>>> X = df[["age", "race"]]
>>> t = np.asarray(df["trt"])
>>> ps = PropensityScore(sample_kwargs={"progressbar": False})
>>> ps.fit(X, t, coords={
...                 'coeffs': ['age', 'race'],
...                 'obs_indx': np.arange(df.shape[0])
...                },
... )
Inference...

Methods

`PropensityScore.__init__`([sample_kwargs])
`PropensityScore.add_coord`(name[, values, ...])	Registers a dimension coordinate with the model.
`PropensityScore.add_coords`(coords, *[, lengths])	Vectorized version of `Model.add_coord`.
`PropensityScore.add_named_variable`(var[, dims])	Add a random graph variable to the named variables of the model.
`PropensityScore.build_model`(X, t, coords)	Defines the PyMC propensity model
`PropensityScore.calculate_cumulative_impact`(impact)
`PropensityScore.calculate_impact`(y_true, y_pred)
`PropensityScore.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).
`PropensityScore.compile_d2logp`([vars, ...])	Compiled log probability density hessian function.
`PropensityScore.compile_dlogp`([vars, jacobian])	Compiled log probability density gradient function.
`PropensityScore.compile_fn`(outs, *[, ...])	Compiles an PyTensor function
`PropensityScore.compile_logp`([vars, ...])	Compiled log probability density function.
`PropensityScore.create_value_var`(rv_var, *, ...)	Create a `TensorVariable` that will be used as the random variable's "value" in log-likelihood graphs.
`PropensityScore.d2logp`([vars, jacobian, ...])	Hessian of the models log-probability w.r.t.
`PropensityScore.debug`([point, fn, verbose])	Debug model function at point.
`PropensityScore.dlogp`([vars, jacobian])	Gradient of the models log-probability w.r.t.
`PropensityScore.eval_rv_shapes`()	Evaluates shapes of untransformed AND transformed free variables.
`PropensityScore.fit`(X, t, coords)	Draw samples from posterior, prior predictive, and posterior predictive distributions.
`PropensityScore.initial_point`([random_seed])	Computes the initial point of the model.
`PropensityScore.logp`([vars, jacobian, sum])	Elemwise log-probability of the model.
`PropensityScore.logp_dlogp_function`([...])	Compile an PyTensor function that computes logp and gradient.
`PropensityScore.make_obs_var`(rv_var, data, ...)	Create a TensorVariable for an observed random variable.
`PropensityScore.name_for`(name)	Checks if name has prefix and adds if needed
`PropensityScore.name_of`(name)	Checks if name has prefix and deletes if needed
`PropensityScore.point_logps`([point, round_vals])	Computes the log probability of point for all random variables in the model.
`PropensityScore.predict`(X)	Predict data given input data X
`PropensityScore.print_coefficients`(labels[, ...])
`PropensityScore.profile`(outs, *[, n, point, ...])	Compiles and profiles an PyTensor function which returns `outs` and takes values of model vars as a dict as an argument.
`PropensityScore.register_data_var`(data[, dims])	Register a data variable with the model.
`PropensityScore.register_rv`(rv_var, name, *)	Register an (un)observed random variable with the model.
`PropensityScore.replace_rvs_by_values`(...)	Clone and replace random variables in graphs with their value variables.
`PropensityScore.score`(X, y)	Score the Bayesian \(R^2\) given inputs `X` and outputs `y`.
`PropensityScore.set_data`(name, values[, coords])	Changes the values of a data variable in the model.
`PropensityScore.set_dim`(name, new_length[, ...])	Update a mutable dimension.
`PropensityScore.set_initval`(rv_var, initval)	Sets an initial value (strategy) for a random variable.
`PropensityScore.shape_from_dims`(dims)
`PropensityScore.to_graphviz`(*[, var_names, ...])	Produce a graphviz Digraph from a PyMC model.
`PropensityScore.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)