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包花粉是学习和评估基于观测数据的有限阶段政策的统一框架。该套方案收集了现有和新的因果政策学习方法,包括双重强力限制的Q学习、政策树学习和成果加权学习。这套方案只考虑现实的政策,处理(近距离)违反积极性的情况。高度灵活的机器学习方法可以通过交叉校准来评估骚扰成分和有效推论政策价值。图书馆围绕一个简单的语法建立,该语法包含四项主要功能政策_data()、政策_def()、政策_learn()以及政策_eval(),用于指定数据结构、界定用户指定的政策、具体规定政策学习方法和评估(了解)政策。该套方案的功能通过大量重现的例子加以说明。