econml.inference.BootstrapInference

class econml.inference.BootstrapInference(n_bootstrap_samples=100, n_jobs=- 1, bootstrap_type='pivot', verbose=0)

Bases: econml.inference._inference.Inference

Inference instance to perform bootstrapping.

This class can be used for inference with any CATE estimator.

Parameters

• n_bootstrap_samples (int, default 100) – How many draws to perform.

• n_jobs (int, default -1) – The maximum number of concurrently running jobs, as in joblib.Parallel.

• verbose (int, default: 0) – Verbosity level

• bootstrap_type (‘percentile’, ‘pivot’, or ‘normal’, default ‘pivot’) – Bootstrap method used to compute results. ‘percentile’ will result in using the empiracal CDF of the replicated computations of the statistics. ‘pivot’ will also use the replicates but create a pivot interval that also relies on the estimate over the entire dataset. ‘normal’ will instead compute a pivot interval assuming the replicates are normally distributed.

__init__(n_bootstrap_samples=100, n_jobs=- 1, bootstrap_type='pivot', verbose=0)

Methods

__init__([n_bootstrap_samples, n_jobs, ...])
ate_inference([X, T0, T1])
ate_interval([X, T0, T1, alpha])
const_marginal_ate_inference([X])
const_marginal_ate_interval([X, alpha])
fit(estimator, *args, **kwargs)	Fits the inference model.
marginal_ate_inference(T[, X])
marginal_ate_interval(T[, X, alpha])
prefit(estimator, *args, **kwargs)	Performs any necessary logic before the estimator's fit has been called.

fit(estimator, *args, **kwargs)

Fits the inference model.

This is called after the estimator’s fit.

prefit(estimator, *args, **kwargs)

Performs any necessary logic before the estimator’s fit has been called.