cea_policy_tree: Train a policy tree after a CEA forest.

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

Description

cea_policy_tree Trains an efficient policy decision tree given a CEA forest (a wrapper for policytree::policy_tree).

Usage

cea_policy_tree(
  forest,
  X,
  WTP = NULL,
  depth = 2,
  ci.level = 0.95,
  robust.se = FALSE
)

Arguments

forest	A trained CEA forest.
X	A covariate matrix containing variables that are to be used in the policy tree.
WTP	Willingness to pay for a one unit increase in the outcome. If NULL, the WTP supplied to the CEA forest is used.
depth	The desired depth for the decision tree.
ci.level	Desired significance level (for confidence intervals).
robust.se	Whether or not robust (sandwich) standard errors are desired. Defaults to FALSE.

Value

Returns a trained policy tree.

References

Athey, S., & Wager, S. (2017). Efficient policy learning. arXiv preprint arXiv:1702.02896.

Examples

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