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.

翻译：R包polle是一个基于观测数据学习和评估有限阶段策略的统一框架。该包实现了因果策略学习的现有及新颖方法合集，包括双稳健受限Q学习、策略树学习和结果加权学习。该包仅考虑现实策略以应对（近）正性违反问题。高度灵活的机器学习方法可用于估计干扰成分，并通过交叉拟合确保策略价值的有效推断。该库围绕简洁语法构建，包含policy_data()、policy_def()、policy_learn()和policy_eval()四个主要函数，分别用于指定数据结构、定义用户自定义策略、设定策略学习方法以及评估（已学习的）策略。通过大量可复现示例展示了该包的功能性。