  The R package polle is a unifying framework for learning and evaluating
finite stage policies based on observational data. The package implements a
collection of existing and novel methods for causal policy learning including
doubly robust restricted Q-learning, policy tree learning, and outcome weighted
learning. The package deals with (near) positivity violations by only
considering realistic policies. Highly flexible machine learning methods can be
used to estimate the nuisance components and valid inference for the policy
value is ensured via cross-fitting. The library is built up around a simple
syntax with four main functions policy_data(), policy_def(), policy_learn(),
and policy_eval() used to specify the data structure, define user-specified
policies, specify policy learning methods and evaluate (learned) policies. The
functionality of the package is illustrated via extensive reproducible
examples.
