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Policy Learning (polle)

Package for evaluating user-specified finite stage policies and learning optimal treatment policies via doubly robust loss functions. Policy learning methods include doubly robust learning of the blip/conditional average treatment effect and sequential policy tree learning. The package also include methods for optimal subgroup analysis. See Nordland and Holst (2022) doi:10.48550/arXiv.2212.02335 for documentation and references.

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Version

Install

install.packages('polle')

Monthly Downloads

1,968

Version

1.6.2

License

Apache License (>= 2)

Maintainer

Andreas Nordland

Last Published

December 4th, 2025

Functions in polle (1.6.2)

get_q_functions

Get Q-functions
get_stage_action_sets

Get Stage Action Sets
get_n

Get Number of Observations
get_id_stage

Get IDs and Stages
get_utility

Get the Utility
get_policy_object

Get Policy Object
nuisance_functions

Nuisance Functions
policy_eval

Policy Evaluation
plot.policy_data

Plot policy data for given policies
policy

Policy-class
partial

Trim Number of Stages
policy_eval_online

Online/Sequential Policy Evaluation
policy_learn

Create Policy Learner
polle-package

polle: Policy Learning
plot.policy_eval

Plot histogram of the influence curve for a policy_eval object
policy_def

Define Policy
policy_data

Create Policy Data Object
sim_multi_stage

Simulate Multi-Stage Data
sim_two_stage

Simulate Two-Stage Data
q_model

q_model class object
sim_single_stage_multi_actions

Simulate Single-Stage Multi-Action Data
predict.nuisance_functions

Predict g-functions and Q-functions
sim_two_stage_multi_actions

Simulate Two-Stage Multi-Action Data
subset_id

Subset Policy Data on ID
sim_single_stage

Simulate Single-Stage Data
reexports

Objects exported from other packages
control_drql

Control arguments for doubly robust Q-learning
control_earl

Control arguments for Efficient Augmentation and Relaxation Learning
control_rwl

Control arguments for Residual Weighted Learning
control_blip

Control arguments for doubly robust blip-learning
conditional

Conditional Policy Evaluation
control_ptl

Control arguments for Policy Tree Learning
copy_policy_data

Copy Policy Data Object
control_owl

Control arguments for Outcome Weighted Learning
c_model

c_model class object
fit_c_functions

Fit Censoring Functions
fit_g_functions

Fit g-functions
get_event

Get event indicators
get_actions

Get Actions
get_K

Get Maximal Stages
get_history_names

Get history variable names
get_id

Get IDs
get_g_functions

Get g-functions
get_action_set

Get Action Set
history

Get History Object
g_model

g_model class object
get_policy_actions

Get Policy Actions
get_policy

Get Policy
get_policy_functions.blip

Get Policy Functions