debias_effects: Get de-biased heterogeneous effect estimates (for custom...

In bonander/CEAforests: Tree-based heterogenous cost-effectiveness and policy learning with individual-level data (beta)

Description

debias_effects Provides de-biased tau estimates based on AIPW (for use in custom explainers, e.g., regression models or GAMs).

Usage

debias_effects(forest, WTP = NULL)

Arguments

forest	A trained CEAforests object.
WTP	The willingness to pay for a one-unit increase in Y. Defaults to the WTP supplied to the CEAforests object unless manually specified.

Value

Returns a matrix of transformed outcomes that can be used in parametric or semi-parametric regressions to explain the results from the forest.

Examples

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bonander/CEAforests documentation built on April 1, 2021, 10:57 a.m.