Type: Package
Title: Constructs Policy Trees from Covariate and Reward Data
Version: 1.0
Description: Constructs optimal policy trees which provide a rule-based treatment prescription policy. Input is covariate and reward data, where, typically, the rewards will be doubly robust reward estimates. This package aims to construct optimal policy trees more quickly than the existing 'policytree' package and is intended to be used alongside that package. For more details see Cussens, Hatamyar, Shah and Kreif (2025) <doi:10.48550/arXiv.2506.15435>.
URL: https://github.com/jcussens/tailoring
Suggests: policytree
Imports: Rcpp (≥ 1.0.7)
LinkingTo: Rcpp
RoxygenNote: 7.3.2
Encoding: UTF-8
License: GPL (≥ 3)
NeedsCompilation: yes
Packaged: 2025-06-20 11:36:28 UTC; uw20605
Author: James Cussens ORCID iD [aut, cre], Julia Hatamyar [ctb], Vishalie Shah [ctb], University of Bristol [cph], MRC [fnd]
Maintainer: James Cussens <james.cussens@bristol.ac.uk>
Repository: CRAN
Date/Publication: 2025-06-24 08:50:06 UTC

fastpolicytree: Constructs Policy Trees from Covariate and Reward Data

Description

A package for constructing optimal policy trees from covariate and reward data. It provides a subset of the functionality of the policytree package but constructs the trees more quickly (particularly for discrete data) and thus allows e.g. deeper trees to be constructed.

Author(s)

Maintainer: James Cussens james.cussens@bristol.ac.uk (ORCID)

Other contributors:

See Also

Useful links:

Construct an optimal policy tree from covariate and reward data

Description

This function accepts almost the same input and generates the same type of output as the policy_tree function in the policytree package. The only difference is that this function has no 'split.step' argument (since it is effectively hard-coded to the value 1).

Usage

fastpolicytree(
  X,
  Gamma,
  depth = 3,
  min.node.size = 1,
  strategy.datatype = 2,
  strategy.find.reward.ub = FALSE,
  strategy.find.dummy.split.reward = FALSE,
  strategy.use.last.rewards = TRUE,
  strategy.use.cutoffs = FALSE,
  strategy.use.cache = TRUE,
  strategy.exploitbinaryvars = TRUE
)

Arguments

X

The covariates used. Dimension N*p where p is the number of features.

Gamma

The rewards for each action. Dimension N*d where d is the number of actions.

depth

The depth of the fitted tree. Default is 3.

min.node.size

An integer indicating the smallest terminal node size permitted. Default is 1.

strategy.datatype

If set to 0 policytree style sorted sets are used to represent datasets during solving. If set to 1 then unsorted sets are used which are sorted 'on demand'. If set to to 2 then the choice of representation is decided automatically. Default is 2 (choice is automatically made).

strategy.find.reward.ub

If TRUE upper bounds on rewards are computed. Default is FALSE

strategy.find.dummy.split.reward

If TRUE then the reward for a dummy split (where the left split has no datapoints) is computed. Default is FALSE.

strategy.use.last.rewards

If TRUE an upper bound on the reward for a split is computed from the reward for the most recent split value for the current covariate. Default is TRUE

strategy.use.cutoffs

If TRUE then tree finding is aborted if it can be deduced that the reward for the tree is beaten by some existing tree. Default is FALSE

strategy.use.cache

If TRUE a cache of optimal trees for (sub-)datasets is used. Default is TRUE

strategy.exploitbinaryvars

If TRUE then covariates with only 2 values are treated specially. Default is TRUE

Value

A policy_tree object.

Examples

X <-  data.frame(
 X1=c(-0.32, 0.16, 0.34, 1.24, 0.22, 0.45, 1.48, 0.65,-0.93,-1.11),
 X2=c(-0.58, 0.90,-0.22, 1.54,-0.57,-1.08,-1.42,-1.98,-0.02, 0.05),
 X3=c(0.70,-1.49, 0.36,-0.05,-0.14, 1.57,-0.18,-1.98,-1.77,-1.25),
 X4=c(0.21, 0.34, 0.60,-0.05,-0.66,-0.69, 0.52, 0.31,-0.03, 1.09),
 X5=c(0.16, 0.96,-1.07,-0.97, 2.02,-0.43,-0.79,-2.08, 1.21, 0.39))
Gamma  <- data.frame(
 control=c(0.8502363,-1.4950411,1.9608062,0.7487925,2.9718517,
  0.8952429,-0.2563680,5.9945581,-1.8485703,-1.2840477),
 treat=c(-2.91607259,-2.25464535, 0.28214637,-0.17284650,-0.09480810,
  1.48786125,2.08600119,-2.05283394,0.72903608,-0.04416392))
tree3  <- fastpolicytree(X,Gamma)
tree3
tree2  <- fastpolicytree(X,Gamma,depth=2)
tree2
# to get a human-readable display of the trees use the
# policytree package...
#library(policytree)
#print(tree3)
#print(tree2)

Returns git hash for compiled C code

Description

Returns git hash for compiled C code

Usage

githash()

Value

A string which is the relevant githash

mirror server hosted at Truenetwork, Russian Federation.